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PostmanFileEditVIewWindowHelpHubSpot rate limit i PostmanFileEditVIewWindowHelpHubSpot rate limit implementation strategy"results":"name": "private-apps-api-calls-daily","usageLimit": 1000000,"currentUsage": 0"collectedAt": "2026-05-07T11:23:01.3627".Distinguished private app limits from OAuth quota constraints ›yes and no — the 1,000,000 ngure 1s real, but it's not counting your OAuth calls.The tell: currentUsage: 0You ve been making calls in Postman (we just contirmed it from the headers), yetcurrentUsage is zero. That's not a sync delav - that endpoint reports in near-real-time. Thereason it's 0 is the field name• nrivate-anns-ani-calls-dailv.This counter tracks legacy private apps installed in the portal, aggregated across all of them.Your Postman token is hitting the APlasan ©Auth app (we know this because Max=110 ).sovour calls don't increment this counter at all.So what does 1.000.000 actually mean here?It's the daily ceiling for private apps in this portal, determined by the portal's tier:•250.000 = Free /Starter• 625.000 = Proressional•1 MMA AA - Entarnrica lor lawor tior ¿ 1y ADIT imit Inaronco addbanySo this portal is either enterprise or nas bougnt add-ons. Ir you ever install a private app intothis portal, that's the budget vou'd be sharing — oross all private apps in the portal, not per-app.Keep going in Claude CodeSwitch to Claude Code and let Claude work directiv in vour reno.distributed-add dailv limits table doesn't adplv to OAuth ados and the API Limit Increase add-Write a message…Opus 4.7 Adaptive v"suppont Dally • In 3omXx Hubspot vYour team is now on the Free plan with 1 admin. You retain editing access and other members are read-only. View team permissions to see who can edit, or upgrade to restore collaboration.GET next off. • POST search •No environmentvCRM Obiects > crm/v3/obiects/{obiect Tvoe) > (obiect Id) > Readl) SaveToaseurl)) /crm/vs/objects/call/4801/1536580/properties=boby.internalmeetingnotescassociations=contacis,deals,companiesE Docs Params • Authorization • Headers Body Scripts SettingsCookiesQuery ParamsKeyValueDescriotionBulk Edit ..v propertiesboby.internalveeuingnotesA comma separated list or the properties to be returned inpropertesnameA comma separated list of the properties to be returned irassociations<string>A comma separated list of obiect tvoes to retrieve associalcompaniesA comma separated list of obiect types to retrieve associalfalsearchivedlfalseWhether to return only results that have been archived,205825333040The name of a property whose values are unique for this olassociationscontacis. deals companiesDescription100% L2Inu/ May 14.20.10UparadeVAIlVariables in requestNo variables used vet. Learn more about variables> All variablesv COLLECtIONs, Contactsv CRM Objectsv crm/vslobiects/obiect Twoe,> U batchv ooiect Id›> • associations/{to Object Type)GET Read20, An error occurred.eg. successful operation>DEL Archive> PATCH Update>GET List> POST Filter, Sort, and Search CRM Objects> CRM Owners> CRM Pinelinec> Dealsv Engagements> D OLD ENGAGEMENTSGET list meetingsPOST search moditied companiesPOST search tackeGET read call> POST soarch callsGET list callsPOST meetings scheduledGET get meetinaPoSt aet link to tack> POST Create Contact with AssociationJournal & webhoooks v4post Get tokenGET det suoscriotionsPOST create subscrintiorGET Journal earliestGET Journal latestgeT httos:nuospot-GET noyt offcotPOST aet Token prodDEL DELETE CLIRCCDIDTION DEP DOPTAIICAMIDONMCNTC) spFcsELOWS@ Connect Git = Concole 5.l TerminResponseHistoryto) Send + Get a successful response# Send + Visualize response# Send + Write testsGiobals Vault Tooks •- m=m...
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PostmanFileEditVIewWindowHelpHubSpot rate limit i PostmanFileEditVIewWindowHelpHubSpot rate limit implementation strategy"results":"name": "private-apps-api-calls-daily","usageLimit": 1000000,"currentUsage": 0"collectedAt": "2026-05-07T11:23:01.3627".Distinguished private app limits from OAuth quota constraints ›yes and no — the 1,000,000 ngure 1s real, but it's not counting your OAuth calls.The tell: currentUsage: 0You ve been making calls in Postman (we just contirmed it from the headers), yetcurrentUsage is zero. That's not a sync delav - that endpoint reports in near-real-time. Thereason it's 0 is the field name• nrivate-anns-ani-calls-dailv.This counter tracks legacy private apps installed in the portal, aggregated across all of them.Your Postman token is hitting the APlasan ©Auth app (we know this because Max=110 ).sovour calls don't increment this counter at all.So what does 1.000.000 actually mean here?It's the daily ceiling for private apps in this portal, determined by the portal's tier:•250.000 = Free /Starter• 625.000 = Proressional•1 MMA AA - Entarnrica lor lawor tior ¿ 1y ADIT imit Inaronco addbanySo this portal is either enterprise or nas bougnt add-ons. Ir you ever install a private app intothis portal, that's the budget vou'd be sharing — oross all private apps in the portal, not per-app.Keep going in Claude CodeSwitch to Claude Code and let Claude work directiv in vour reno.distributed-add dailv limits table doesn't adplv to OAuth ados and the API Limit Increase add-Write a message…Opus 4.7 Adaptive v"suppont Dally • In 3omXx Hubspot vYour team is now on the Free plan with 1 admin. You retain editing access and other members are read-only. View team permissions to see who can edit, or upgrade to restore collaboration.GET next off. • POST search •No environmentvCRM Obiects > crm/v3/obiects/{obiect Tvoe) > (obiect Id) > Readl) SaveToaseurl)) /crm/vs/objects/call/4801/1536580/properties=boby.internalmeetingnotescassociations=contacis,deals,companiesE Docs Params • Authorization • Headers Body Scripts SettingsCookiesQuery ParamsKeyValueDescriotionBulk Edit ..v propertiesboby.internalveeuingnotesA comma separated list or the properties to be returned inpropertesnameA comma separated list of the properties to be returned irassociations<string>A comma separated list of obiect tvoes to retrieve associalcompaniesA comma separated list of obiect types to retrieve associalfalsearchivedlfalseWhether to return only results that have been archived,205825333040The name of a property whose values are unique for this olassociationscontacis. deals companiesDescription100% L2Inu/ May 14.20.10UparadeVAIlVariables in requestNo variables used vet. Learn more about variables> All variablesv COLLECtIONs, Contactsv CRM Objectsv crm/vslobiects/obiect Twoe,> U batchv ooiect Id›> • associations/{to Object Type)GET Read20, An error occurred.eg. successful operation>DEL Archive> PATCH Update>GET List> POST Filter, Sort, and Search CRM Objects> CRM Owners> CRM Pinelinec> Dealsv Engagements> D OLD ENGAGEMENTSGET list meetingsPOST search moditied companiesPOST search tackeGET read call> POST soarch callsGET list callsPOST meetings scheduledGET get meetinaPoSt aet link to tack> POST Create Contact with AssociationJournal & webhoooks v4post Get tokenGET det suoscriotionsPOST create subscrintiorGET Journal earliestGET Journal latestgeT httos:nuospot-GET noyt offcotPOST aet Token prodDEL DELETE CLIRCCDIDTION DEP DOPTAIICAMIDONMCNTC) spFcsELOWS@ Connect Git = Concole 5.l TerminResponseHistoryto) Send + Get a successful response# Send + Visualize response# Send + Write testsGiobals Vault Tooks •- m=m...
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PostmanFileEditVIewWindowHelpHubSpot rate limit PostmanFileEditVIewWindowHelpHubSpot rate limit implementation strategy"results": ["name": "private-apps-api-calls-daily","usageLimit": 1000000,"currentUsage": 0"collectedAt": "2026-05-07T11:23:01.362Z",Distinguished private app limits from OAuth quota constraints ›yes and no — the 1,000,000 ngure is real, but it's not counting your OAuth calls.The tell: currentUsage: 0You've been making calls in Postman (we just confirmed it from the headers), yetcurrentUsage is zero. That's not a sync delav - that endpoint reports in near-real-time. Thereason it's 0 is the field name• nrivate-anns-ani-calls-dailv.This counter tracks legacy private apps installed in the portal, aggregated across all of them.Your Postman token is hitting the APlasan ©Auth app (we know this because Max=110 ).sovour calls don't increment this counter at all.So what does 1,000,000 actually mean here?It's the daily ceiling for private apps in this portal, determined by the portal's tier:• 250,000 = Free / Starter• 625.000 = Proressional• 1.000.000 = Enterprise (or lower tier + 1x API Limit Increase add-on)So this portal is either enterprise or has bougnt add-ons. Ir you ever install a private app intothis portal, that's the budget you'd be sharing - c-ross all private apps in the portal, not per-app.Keep going in Claude CodeSwitch to Claude Code and let Claude work directiv in vour reno.distributed-aDD daily limits table doesn't aDplv to OAuth aDDs. and the API Limit Increase add-Write a message…Opus 4.7 Adaptive"suppont Dally • In 3omYour team is now on the Free plan with 1 admin. You retain editing access and other members are read-only. View team permissions to see who can edit, or upgrade to restore collaboration.GET next off. • POST search •GET Read •No environmentCRM Objects › crm/v3/objects/(object Type) › (object Id) › Read) SaveToaseurl)) /crm/vs/objects/call/4801/1536580/properties=boby.internalmeetingnotescassociations=contacis,deals,companies# Docs Params • Authorization • Headers 9 Body Scripts SettingsQuery ParamsKeyv propertiesValuepropertesboby.internalmeetingNotesname<string>companiesfalsefalse205825333040v associationscontacis deals companies7> HTMLvCookiesDescriotionBulk Edit ..A comma separated list or the properties to be returned inA comma separated list of the properties to be returned inA comma separated list of object types to retrieve associatA comma separated list of obiect types to retrieve associalWhether to return only results that have been archived.The name of a proverty whose values are unique for this olDescription404 Not Found • 180 ms • 1.21 KB - G eg. Save Response ...=a1D8100% L2VAIlVariables in requestC baseUrlo token• All VarlablesThu 7 May 14:25:21Uparadev COLLECtIONs, ContactsCRM Objectsv crm/vslobiects/obiect Twoe,> O batchv ooiect Id›› [ associations/(to Object Type)GET ReadE. An error occurred.eg. successful operation> DEL Archive> PATCH Update>GET List> PosT Filter, Sort, and Search CRM Objects> CRM Owners> CRM Pipelines> Dealsv Engagements> D OLD ENGAGEMENTSGET list meetingsPoSt search tasksGET read call> PoST search callsGET list callsPoST meetings scheduledGET get meetinaPost get link to task> POST Create Contact with AssociationJournal & webhoooks v4POST Get tokenGET det subscriotionsPOST create subscriptionGET Journal earliestGET Journal latestgeT httos:nuospot-Gst noyt offcotPOST aet Token prodDEL DELETE CLIRCCDIDTION DEP DOPTAIICAMIDONMCNTC> SPFCS> FLOWS@ Connect Git = Concole 5.) Terminhttos:/lapi.hubaoCNeR-JHgMxIZQINQ…..PreviewY^ Debua with Al l v<meta htto-equiv="Content-Tvoe" content="text/html.charset=utf-8" /><title>Error 404 Not Found</title>ALASUTTO CODOD ЛЛЛИЛLОSGlobals Vault Tools S000...
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PostmanFileEditVIewWindowHelpHubSpot rate limit PostmanFileEditVIewWindowHelpHubSpot rate limit implementation strategy"results": ["name": "private-apps-api-calls-daily","usageLimit": 1000000,"currentUsage": 0"collectedAt": "2026-05-07T11:23:01.362Z",Distinguished private app limits from OAuth quota constraints ›yes and no — the 1,000,000 ngure is real, but it's not counting your OAuth calls.The tell: currentUsage: 0You've been making calls in Postman (we just confirmed it from the headers), yetcurrentUsage is zero. That's not a sync delav - that endpoint reports in near-real-time. Thereason it's 0 is the field name• nrivate-anns-ani-calls-dailv.This counter tracks legacy private apps installed in the portal, aggregated across all of them.Your Postman token is hitting the APlasan ©Auth app (we know this because Max=110 ).sovour calls don't increment this counter at all.So what does 1,000,000 actually mean here?It's the daily ceiling for private apps in this portal, determined by the portal's tier:• 250,000 = Free / Starter• 625.000 = Proressional• 1.000.000 = Enterprise (or lower tier + 1x API Limit Increase add-on)So this portal is either enterprise or has bougnt add-ons. Ir you ever install a private app intothis portal, that's the budget you'd be sharing - c-ross all private apps in the portal, not per-app.Keep going in Claude CodeSwitch to Claude Code and let Claude work directiv in vour reno.distributed-aDD daily limits table doesn't aDplv to OAuth aDDs. and the API Limit Increase add-Write a message…Opus 4.7 Adaptive"suppont Dally • In 3omYour team is now on the Free plan with 1 admin. You retain editing access and other members are read-only. View team permissions to see who can edit, or upgrade to restore collaboration.GET next off. • POST search •GET Read •No environmentCRM Objects › crm/v3/objects/(object Type) › (object Id) › Read) SaveToaseurl)) /crm/vs/objects/call/4801/1536580/properties=boby.internalmeetingnotescassociations=contacis,deals,companies# Docs Params • Authorization • Headers 9 Body Scripts SettingsQuery ParamsKeyv propertiesValuepropertesboby.internalmeetingNotesname<string>companiesfalsefalse205825333040v associationscontacis deals companies7> HTMLvCookiesDescriotionBulk Edit ..A comma separated list or the properties to be returned inA comma separated list of the properties to be returned inA comma separated list of object types to retrieve associatA comma separated list of obiect types to retrieve associalWhether to return only results that have been archived.The name of a proverty whose values are unique for this olDescription404 Not Found • 180 ms • 1.21 KB - G eg. Save Response ...=a1D8100% L2VAIlVariables in requestC baseUrlo token• All VarlablesThu 7 May 14:25:21Uparadev COLLECtIONs, ContactsCRM Objectsv crm/vslobiects/obiect Twoe,> O batchv ooiect Id›› [ associations/(to Object Type)GET ReadE. An error occurred.eg. successful operation> DEL Archive> PATCH Update>GET List> PosT Filter, Sort, and Search CRM Objects> CRM Owners> CRM Pipelines> Dealsv Engagements> D OLD ENGAGEMENTSGET list meetingsPoSt search tasksGET read call> PoST search callsGET list callsPoST meetings scheduledGET get meetinaPost get link to task> POST Create Contact with AssociationJournal & webhoooks v4POST Get tokenGET det subscriotionsPOST create subscriptionGET Journal earliestGET Journal latestgeT httos:nuospot-Gst noyt offcotPOST aet Token prodDEL DELETE CLIRCCDIDTION DEP DOPTAIICAMIDONMCNTC> SPFCS> FLOWS@ Connect Git = Concole 5.) Terminhttos:/lapi.hubaoCNeR-JHgMxIZQINQ…..PreviewY^ Debua with Al l v<meta htto-equiv="Content-Tvoe" content="text/html.charset=utf-8" /><title>Error 404 Not Found</title>ALASUTTO CODOD ЛЛЛИЛLОSGlobals Vault Tools S000...
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PostmanFileEditVIewWindowHelpHubSpot rate limit i PostmanFileEditVIewWindowHelpHubSpot rate limit implementation strategy"results":"name": "private-apps-api-calls-daily","usageLimit": 1000000,"currentUsage": 0"collectedAt": "2026-05-07T11:23:01.3627".Distinguished private app limits from OAuth quota constraints ›yes and no — the 1,000,000 ngure is real, but it's not counting your OAuth calls.The tell: currentUsage: 0You ve been making calls in Postman (we just contirmed it from the headers), vetcurrentUsage is zero. That's not a sync delav — that endpoint reports in near-real-time. Thereason it's 0 is the field name• nrivate-anns-ani-calls-dailv.This counter tracks legacy private apps installed in the portal, aggregated across all of them.Your Postman token is hitting the APlasan ©Auth app (we know this because Max=110 ).sovour calls don't increment this counter at all.So what does 1.000.000 actually mean here?It's the daily ceiling for private apps in this portal, determined by the portal's tier:•250.000 = Free /Starter• 625.000 = Proressional•1 MMA AAA - Entarnrica lor lawor tior ¿ 1y A DIT imit Inaronco oddbanySo this portal is either enterprise or nas bougnt add-ons. Ir you ever install a private app intothis portal, that's the budget vou'd be sharing — coross all private apps in the portal, not per-app.Keep going in Claude CodeSwitch to Claude Code and let Claude work directiv in vour reno.distributed-add dailv limits table doesn't adplv to OAuth apds and the API Limit Increase add-Write a message…Opus 4.7 Adaptive v"suppont Dally • In 3omXx Hubspot vYour team is now on the Free plan with 1 admin. You retain editing access and other members are read-only. View team permissions to see who can edit, or upgrade to restore collaboration.v COLLECtIONs, Contactsv CRM Objectsv crm/vslobiects/obiect Twoe,> U batchv ooiect Id›> • associations/{to Object Type)GET Read20: An error occurred.eg. successful operation>DEL Archive> PATCH Update>GET List> POST Filter, Sort, and Search CRM Objects> CRM Owners> CRM Pinelinec> Dealsv Engagements> D OLD ENGAGEMENTSGET list meetingsPOST search moditied companiesPOST search tackeGET read call> POST soarch callsGET list callsPOST meetings scheduledGET get meetinaPoSt aet link to tack> POST Create Contact with Association> HubsnotJournal & webhoooks v4post Get tokenGET det suoscriotionsPOST create subscrintiorGET Journal earliestGET Journal latestGeT httos:nuosoot-yGET next offsetPOST aet Token prodDEL DELETE CLIRCCDIDTION DEP DOPTAIICAMIDONMCNTC) spFcsELOWS$ Connect Git @ Console TermirGET next off. • POST search •GET read ciNo environmentCRM Obiects > crm/v3/obiects/{obiect Tvoel > (obiect Id) > Read) SaveToaseurl)) /crm/vs/objects/call/4801/1536580/properties=boby.internalmeetingnotescassociations=contacis,deals,companiesE Docs Params • Authorization • Headers 9 Body Scripts SettingsCookiesQuery ParamsKeyv propertiesDescriotionBulk Edit ..propertesboby.internalmeetingNotesname<string>companiesfalsefalse205825333040A comma separated list or the properties to be returned inA comma separated list of the properties to be returned inA comma separated list of obiect tvoes to retrieve associalA comma separated list of obiect types to retrieve associalWhether to return only results that have been archived,The name of a property whose values are unique for this olv associationscontacis. deals companiesDescriptionHeafers 21 lest Results404 Not Found 180 ms • 1.21 KB •e.g. save kesponse:statusdateThu. 07 May 2026 11:25•20 GMTcontent-typewovt/htmlcharcotcica.0950.1cf-ray9f7fdd086a8f8428-SOFct-cache-statusDYNAMICcache-controlmust-revalidate,no-cache,no-storestrict-transnort-securitvmax-ade-31536000- includeSubDomains: preloadaccess-control-allow-credentialsorigin, Accept-Encodinafalceserver-timinalhcid:desc="019e022f-6184-7cff-9b0a-c658ca38753c" cfr.desc="9f7fdd0871753402-SOF*x-hubspot-correlation-id019e022f-6184-7cff-9b0a-c658ca38753cy-hubenot-notfound10000wwnhonrrashlmmawx-hubspot-ratelimit-remainingx-hubspot-ratelimit-secondlv-remainingrenort-tof"endnoints".f("url"."httnc+|Ma nel.cloudflare com\renort|w1?c-yhsyQCOdl4%2FwNDIlviAp"success fraction":0.01."report_to"-"cf-nel""max age":604800}100% L2VAIlVariables in requestC baseUrlo token• All VarlablesInu / May 14-20-24httos:/lapi.hubaoCNeR-JHaMxlZoiNd.Giobals Vault Tooks •- m=m...
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PostmanFileEditVIewWindowHelpHubSpot rate limit i PostmanFileEditVIewWindowHelpHubSpot rate limit implementation strategy"results":"name": "private-apps-api-calls-daily","usageLimit": 1000000,"currentUsage": 0"collectedAt": "2026-05-07T11:23:01.3627".Distinguished private app limits from OAuth quota constraints ›yes and no — the 1,000,000 ngure is real, but it's not counting your OAuth calls.The tell: currentUsage: 0You ve been making calls in Postman (we just contirmed it from the headers), vetcurrentUsage is zero. That's not a sync delav — that endpoint reports in near-real-time. Thereason it's 0 is the field name• nrivate-anns-ani-calls-dailv.This counter tracks legacy private apps installed in the portal, aggregated across all of them.Your Postman token is hitting the APlasan ©Auth app (we know this because Max=110 ).sovour calls don't increment this counter at all.So what does 1.000.000 actually mean here?It's the daily ceiling for private apps in this portal, determined by the portal's tier:•250.000 = Free /Starter• 625.000 = Proressional•1 MMA AAA - Entarnrica lor lawor tior ¿ 1y A DIT imit Inaronco oddbanySo this portal is either enterprise or nas bougnt add-ons. Ir you ever install a private app intothis portal, that's the budget vou'd be sharing — coross all private apps in the portal, not per-app.Keep going in Claude CodeSwitch to Claude Code and let Claude work directiv in vour reno.distributed-add dailv limits table doesn't adplv to OAuth apds and the API Limit Increase add-Write a message…Opus 4.7 Adaptive v"suppont Dally • In 3omXx Hubspot vYour team is now on the Free plan with 1 admin. You retain editing access and other members are read-only. View team permissions to see who can edit, or upgrade to restore collaboration.v COLLECtIONs, Contactsv CRM Objectsv crm/vslobiects/obiect Twoe,> U batchv ooiect Id›> • associations/{to Object Type)GET Read20: An error occurred.eg. successful operation>DEL Archive> PATCH Update>GET List> POST Filter, Sort, and Search CRM Objects> CRM Owners> CRM Pinelinec> Dealsv Engagements> D OLD ENGAGEMENTSGET list meetingsPOST search moditied companiesPOST search tackeGET read call> POST soarch callsGET list callsPOST meetings scheduledGET get meetinaPoSt aet link to tack> POST Create Contact with Association> HubsnotJournal & webhoooks v4post Get tokenGET det suoscriotionsPOST create subscrintiorGET Journal earliestGET Journal latestGeT httos:nuosoot-yGET next offsetPOST aet Token prodDEL DELETE CLIRCCDIDTION DEP DOPTAIICAMIDONMCNTC) spFcsELOWS$ Connect Git @ Console TermirGET next off. • POST search •GET read ciNo environmentCRM Obiects > crm/v3/obiects/{obiect Tvoel > (obiect Id) > Read) SaveToaseurl)) /crm/vs/objects/call/4801/1536580/properties=boby.internalmeetingnotescassociations=contacis,deals,companiesE Docs Params • Authorization • Headers 9 Body Scripts SettingsCookiesQuery ParamsKeyv propertiesDescriotionBulk Edit ..propertesboby.internalmeetingNotesname<string>companiesfalsefalse205825333040A comma separated list or the properties to be returned inA comma separated list of the properties to be returned inA comma separated list of obiect tvoes to retrieve associalA comma separated list of obiect types to retrieve associalWhether to return only results that have been archived,The name of a property whose values are unique for this olv associationscontacis. deals companiesDescriptionHeafers 21 lest Results404 Not Found 180 ms • 1.21 KB •e.g. save kesponse:statusdateThu. 07 May 2026 11:25•20 GMTcontent-typewovt/htmlcharcotcica.0950.1cf-ray9f7fdd086a8f8428-SOFct-cache-statusDYNAMICcache-controlmust-revalidate,no-cache,no-storestrict-transnort-securitvmax-ade-31536000- includeSubDomains: preloadaccess-control-allow-credentialsorigin, Accept-Encodinafalceserver-timinalhcid:desc="019e022f-6184-7cff-9b0a-c658ca38753c" cfr.desc="9f7fdd0871753402-SOF*x-hubspot-correlation-id019e022f-6184-7cff-9b0a-c658ca38753cy-hubenot-notfound10000wwnhonrrashlmmawx-hubspot-ratelimit-remainingx-hubspot-ratelimit-secondlv-remainingrenort-tof"endnoints".f("url"."httnc+|Ma nel.cloudflare com\renort|w1?c-yhsyQCOdl4%2FwNDIlviAp"success fraction":0.01."report_to"-"cf-nel""max age":604800}100% L2VAIlVariables in requestC baseUrlo token• All VarlablesInu / May 14-20-24httos:/lapi.hubaoCNeR-JHaMxlZoiNd.Giobals Vault Tooks •- m=m...
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PostmanFileEditVIeWWindowHelpHubSpot rate limit i PostmanFileEditVIeWWindowHelpHubSpot rate limit implementation strategy"results":"name": "private-apps-api-calls-daily","usageLimit": 1000000,"currentUsage": 0"collectedAt": "2026-05-07T11:23:01.3627".Distinguished private app limits from OAuth quota constraints >yes and no — the 1,000,000 ngure 1s real, but it's not counting your OAuth calls.The tell: currentUsage: 0You ve been making calls in Postman (we just contirmed it from the headers), yetcurrentUsage is zero. That's not a sync delav - that endpoint reports in near-real-time. Thereason it's 0 is the field name• nrivate-anns-ani-calls-dailv.This counter tracks legacy private apps installed in the portal, aggregated across all of them.Your Postman token is hitting the APlasan ©Auth app (we know this because Max=110 ).sovour calls don't increment this counter at all.So what does 1.000.000 actually mean here?It's the daily ceiling for private apps in this portal, determined by the portal's tier:•250.000 = Free /Starter• 625.000 = Proressional•1 MMA AA - Entarnrica lor lawor tior ¿ 1y ADIT imit Inaronco addbanySo this portal is either enterprise or nas bougnt add-ons. Ir you ever install a private app intothis portal, that's the budget vou'd be sharing — oross all private apps in the portal, not per-app.Keep going in Claude CodeSwitch to Claude Code and let Claude work directiv in vour reno.distributed-add dailv limits table doesn't adplv to OAuth ados and the API Limit Increase add-Write a message…Opus 4.7 Adaptive v"suppont Dally • In 3omXx Hubspot vYour team is now on the Free plan with 1 admin. You retain editing access and other members are read-only. View team permissions to see who can edit, or upgrade to restore collaboration.GET next off. • POST search •No environmentvCRM Obiects > crm/v3/obiects/{obiect Tvoe) > (obiect Id) > Readl) SaveToaseurl)) /crm/vs/objects/call/4801/1536580/properties=boby.internalmeetingnotescassociations=contacis,deals,companiesJucanceE Docs Params • Authorization• Headers 9 Body ScriptsSettingsCookiesQuery ParamsKeyDescriotionBulk Edit ..v propertiesboby.internalmeetingNotesA comma separated list or the properties to be returned inpropertesnameA comma separated list of the properties to be returned in<string>A comma separated list of obiect tvoes to retrieve associalcompaniesA comma separated list of obiect types to retrieve associalfalsefalseWhether to return only results that have been archived,205825333040The name of a property whose values are unique for this olv associationscontacis. deals companiesDescription100% L2VAIlcbaseUrlo tokenInu / May 14-20.24httos:/lapi.hubaoCNeR-JHaMxlZoiNd.• All Varlablesv COLLECtIONs, Contactsv CRM Objectsv crm/vslobiects/obiect Twoe,> U batchv ooiect Id›> • associations/{to Object Type)GET Read20, An error occurred.eg. successful operation>DEL Archive> PATCH Update>GET List> POST Filter, Sort, and Search CRM Objects> CRM Owners> CRM Pinelinec> Dealsv Engagements> D OLD ENGAGEMENTSGET list meetingsPOST search moditied companiesPOST search tackeGET read call> POST soarch callsGET list callsPOST meetings scheduledGET get meetinaPoSt aet link to tack> POST Create Contact with AssociationJournal & webhoooks v4post Get tokenGET det suoscriotionsPOST create subscrintiorGET Journal earliestGET Journal latestGeT httos:nuosoot-yGst noyt offcotPOST aet Token prodDEL DEIETE CLIRCCPIDTION DEP DOPTAIICAMIDONMCNTC) spFcsELOWS§ Connect Git E Console 2 TerminaHeaders 21 lest ResultsThu. 07 May 2026 11:25:20 GMTIDYNAMIC019e022f-6184-7cff-9b0a-c658ca38753cGiobals Vault Tooks •- m=m...
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PostmanFileEditVIeWWindowHelpHubSpot rate limit i PostmanFileEditVIeWWindowHelpHubSpot rate limit implementation strategy"results":"name": "private-apps-api-calls-daily","usageLimit": 1000000,"currentUsage": 0"collectedAt": "2026-05-07T11:23:01.3627".Distinguished private app limits from OAuth quota constraints >yes and no — the 1,000,000 ngure 1s real, but it's not counting your OAuth calls.The tell: currentUsage: 0You ve been making calls in Postman (we just contirmed it from the headers), yetcurrentUsage is zero. That's not a sync delav - that endpoint reports in near-real-time. Thereason it's 0 is the field name• nrivate-anns-ani-calls-dailv.This counter tracks legacy private apps installed in the portal, aggregated across all of them.Your Postman token is hitting the APlasan ©Auth app (we know this because Max=110 ).sovour calls don't increment this counter at all.So what does 1.000.000 actually mean here?It's the daily ceiling for private apps in this portal, determined by the portal's tier:•250.000 = Free /Starter• 625.000 = Proressional•1 MMA AA - Entarnrica lor lawor tior ¿ 1y ADIT imit Inaronco addbanySo this portal is either enterprise or nas bougnt add-ons. Ir you ever install a private app intothis portal, that's the budget vou'd be sharing — oross all private apps in the portal, not per-app.Keep going in Claude CodeSwitch to Claude Code and let Claude work directiv in vour reno.distributed-add dailv limits table doesn't adplv to OAuth ados and the API Limit Increase add-Write a message…Opus 4.7 Adaptive v"suppont Dally • In 3omXx Hubspot vYour team is now on the Free plan with 1 admin. You retain editing access and other members are read-only. View team permissions to see who can edit, or upgrade to restore collaboration.GET next off. • POST search •No environmentvCRM Obiects > crm/v3/obiects/{obiect Tvoe) > (obiect Id) > Readl) SaveToaseurl)) /crm/vs/objects/call/4801/1536580/properties=boby.internalmeetingnotescassociations=contacis,deals,companiesJucanceE Docs Params • Authorization• Headers 9 Body ScriptsSettingsCookiesQuery ParamsKeyDescriotionBulk Edit ..v propertiesboby.internalmeetingNotesA comma separated list or the properties to be returned inpropertesnameA comma separated list of the properties to be returned in<string>A comma separated list of obiect tvoes to retrieve associalcompaniesA comma separated list of obiect types to retrieve associalfalsefalseWhether to return only results that have been archived,205825333040The name of a property whose values are unique for this olv associationscontacis. deals companiesDescription100% L2VAIlcbaseUrlo tokenInu / May 14-20.24httos:/lapi.hubaoCNeR-JHaMxlZoiNd.• All Varlablesv COLLECtIONs, Contactsv CRM Objectsv crm/vslobiects/obiect Twoe,> U batchv ooiect Id›> • associations/{to Object Type)GET Read20, An error occurred.eg. successful operation>DEL Archive> PATCH Update>GET List> POST Filter, Sort, and Search CRM Objects> CRM Owners> CRM Pinelinec> Dealsv Engagements> D OLD ENGAGEMENTSGET list meetingsPOST search moditied companiesPOST search tackeGET read call> POST soarch callsGET list callsPOST meetings scheduledGET get meetinaPoSt aet link to tack> POST Create Contact with AssociationJournal & webhoooks v4post Get tokenGET det suoscriotionsPOST create subscrintiorGET Journal earliestGET Journal latestGeT httos:nuosoot-yGst noyt offcotPOST aet Token prodDEL DEIETE CLIRCCPIDTION DEP DOPTAIICAMIDONMCNTC) spFcsELOWS§ Connect Git E Console 2 TerminaHeaders 21 lest ResultsThu. 07 May 2026 11:25:20 GMTIDYNAMIC019e022f-6184-7cff-9b0a-c658ca38753cGiobals Vault Tooks •- m=m...
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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
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10:42
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Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
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You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
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Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =...
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That's the limit the 429 in your example is hitting (","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"secondly limit","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"on","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/crm/v3/objects/contact/search","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"). It's a separate bucket from the 190/10s, and critically, search responses do not include the","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-*","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"headers","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— you can't read remaining capacity off the response, you have to track it yourself.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Non-search endpoints return","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Max","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", and","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Interval-Milliseconds","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". The daily headers are not returned for OAuth-authenticated calls","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— that's enforced for marketplace listing, but it's also a sane operational target.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"So you have","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"four buckets","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Where to store it","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Where to store it","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"INCR","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"EXPIRE","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"plus Lua scripting for true CAS semantics.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Key layout I'd use:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:search:{portalId} ZSET (sliding 1s)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:meta:{portalId} HASH (last seen headers)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The two short-window buckets are sliding-window-log (Redis ZSET, score =","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"microtime","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The daily bucket is a plain","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"INCR","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with TTL set to seconds-until-midnight in","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"HubSpot's account timezone","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to read it","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to read it","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Two-phase: optimistic pre-check, authoritative post-update.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Before a call, run the Lua acquire script. If it returns \"denied,\" sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"After the call, parse","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Max","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Interval-Milliseconds","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and store them in","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:meta:{portalId}","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"For search specifically, since headers don't come back, the local counter","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"is","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"not","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"on 429 (you really did make that request).","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to work with multiple jobs","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to work with multiple jobs","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Concurrency cap","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"via","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Queue::throttle()","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Per-tenant fairness","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Priority lanes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot-priority","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot-bulk","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Backoff on 429","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— respect the","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry-After","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"header. If absent, exponential with jitter (e.g.,","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"min(2^attempt * 250ms, 30s) ± 20%","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to work with paginated requests","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to work with paginated requests","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The trap is treating \"fetch all\" as one logical operation. Each page is its own API call and competes for tokens with everything else.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Two practical rules:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Don't hold the worker idle while paginating.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"processing","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"of page N as a separate job, and queue a \"fetch page N+1\" job with a delay equal to the wait time. Each page becomes its own atomic unit.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Always batch where HubSpot offers it.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/{type}/batch/{read|update|create}","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"accepts up to 100 IDs per call. For your \"patching IDs\" flow this is the difference between 600 calls and 6.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Walkthrough: 600 opportunities","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Walkthrough: 600 opportunities","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Sane flow:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Identify deltas","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— if you can avoid Search at all, do. Use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Batch read","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"current state —","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/deals/batch/read","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with 100 IDs per call → 6 calls. Burst weight 6.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Compute diff","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"in your code (no API).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Batch update","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"—","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/deals/batch/update","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with 100 per call → 6 calls.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Associations","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"—","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v4/associations/{from}/{to}/batch/create","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"if needed → another few batch calls.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Recognition: pre-check denies if","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"<weight>","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"slots aren't free; on 429 the response surfaces","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"policyName","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(DAILY/SECONDLY) — log it, push the job back with","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry-After","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"delay, increment a metric so you can alarm on sustained 429 rate.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Pitfalls & bottlenecks worth pre-empting","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Pitfalls & bottlenecks worth pre-empting","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Daily quota is shared across the whole portal","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search is uniquely fragile","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":": separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hs_object_id","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"ranges or by","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"windows.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry storms after HubSpot incidents","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Webhook → sync → webhook loops","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"jy_last_sync_id","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":") and ignore webhooks where it just changed.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Token refresh","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"expires_in - 60s","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", single-flight the refresh (only one worker refreshes; others wait on a lock).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search latency for fresh writes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Clock skew","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— Redis is authoritative. Don't use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"time()","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"from each worker for window scoring; use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"redis.call('TIME')","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"inside the Lua script.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Dead workers with held tokens","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The 5% error rule","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Phantom contention with Laravel","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"RateLimited","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"middleware","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":": it's per-job-class by default, not per-portal. If your","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"SyncDealJob","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"UpdateActivityJob","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"HubspotRateLimiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"service that everything routes through.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give positive feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give negative feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"You said: What will happen if there are two different types of rate limits?","depth":20,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"You said: What will happen if there are two different types of rate limits?","depth":21,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"What will happen if there are two different types of rate limits?","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search requests that have 5 requests per second","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"13:40","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Edit","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"Claude responded: They're independent buckets — with one shared bucket on top.","depth":21,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Claude responded: They're independent buckets — with one shared bucket on top.","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Analyzed independent rate limit buckets and their practical interactions","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Analyzed independent rate limit buckets and their practical interactions","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"They're independent buckets — with one shared bucket on top.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"The three buckets and what each call hits","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"The three buckets and what each call hits","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"┌──────────────────┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search request ──→ │ Search: 5/sec │ ──┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"└──────────────────┘ │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"├──→ ┌─────────────────┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"┌──────────────────┐ │ │ Daily: 250k–1M │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"└──────────────────┘ └─────────────────┘","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"What this means practically","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"What this means practically","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting search secondly limit (5/s):","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Only further","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/search","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting burst limit (190/10s):","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting daily limit:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"So can one type of request block the other?","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"So can one type of request block the other?","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search → Burst:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"No.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Burst → Search:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"No.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Saturating burst leaves Search's 5/s untouched.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Either → Daily:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Yes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", but only by contributing to the same daily counter. It's volume that kills you here, not the type.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"In theory you can sustain ~50 search + ~190 other =","depth":25,"on_screen":false,"role_description":"text"}]...
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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
Show more
10:42
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Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
Copy
Give positive feedback
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Retry
You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
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Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =...
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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
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Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
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hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
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You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
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Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
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┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
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php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.
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You said: Ok, so what will happen each time I make a request?
You said: Ok, so what will happen each time I make a request?
Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?
13:44
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Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Analyzed per-request overhead concerns and quantified Redis performance impact
Analyzed per-request overhead concerns and quantified Redis performance impact
Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out. But the overhead is much smaller than it sounds, and importantly, it's nowhere near your bottleneck.
What "two buckets per request" actually costs
What "two buckets per request" actually costs
Each request makes
one round trip to Redis
— not two — because you fold both bucket checks into a single Lua script that runs atomically server-side:
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lua
-- KEYS[1] = burst bucket key (or search)
-- KEYS[2] = daily bucket key
-- ARGV[1] = now (ms) ARGV[2] = window_ms
-- ARGV[3] = bucket_max ARGV[4] = daily_max
-- ARGV[5] = request_id ARGV[6] = daily_ttl
-- Trim sliding window
redis
.
call
(
'ZREMRANGEBYSCORE'
,
KEYS
[
1
]
,
0
,
ARGV
[
1
]
-
ARGV
[...
|
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Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The daily bucket is a plain","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"INCR","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with TTL set to seconds-until-midnight in","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"HubSpot's account timezone","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to read it","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to read it","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Two-phase: optimistic pre-check, authoritative post-update.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Before a call, run the Lua acquire script. If it returns \"denied,\" sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"After the call, parse","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Max","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Interval-Milliseconds","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and store them in","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:meta:{portalId}","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"For search specifically, since headers don't come back, the local counter","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"is","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"not","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"on 429 (you really did make that request).","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to work with multiple jobs","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to work with multiple jobs","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Concurrency cap","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"via","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Queue::throttle()","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Per-tenant fairness","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Priority lanes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot-priority","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot-bulk","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Backoff on 429","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— respect the","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry-After","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"header. If absent, exponential with jitter (e.g.,","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"min(2^attempt * 250ms, 30s) ± 20%","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to work with paginated requests","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to work with paginated requests","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The trap is treating \"fetch all\" as one logical operation. Each page is its own API call and competes for tokens with everything else.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Two practical rules:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Don't hold the worker idle while paginating.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"processing","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"of page N as a separate job, and queue a \"fetch page N+1\" job with a delay equal to the wait time. Each page becomes its own atomic unit.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Always batch where HubSpot offers it.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/{type}/batch/{read|update|create}","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"accepts up to 100 IDs per call. For your \"patching IDs\" flow this is the difference between 600 calls and 6.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Walkthrough: 600 opportunities","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Walkthrough: 600 opportunities","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Sane flow:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Identify deltas","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— if you can avoid Search at all, do. Use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Batch read","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"current state —","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/deals/batch/read","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with 100 IDs per call → 6 calls. Burst weight 6.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Compute diff","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"in your code (no API).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Batch update","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"—","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/deals/batch/update","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with 100 per call → 6 calls.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Associations","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"—","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v4/associations/{from}/{to}/batch/create","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"if needed → another few batch calls.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Recognition: pre-check denies if","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"<weight>","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"slots aren't free; on 429 the response surfaces","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"policyName","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(DAILY/SECONDLY) — log it, push the job back with","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry-After","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"delay, increment a metric so you can alarm on sustained 429 rate.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Pitfalls & bottlenecks worth pre-empting","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Pitfalls & bottlenecks worth pre-empting","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Daily quota is shared across the whole portal","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search is uniquely fragile","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":": separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hs_object_id","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"ranges or by","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"windows.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry storms after HubSpot incidents","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Webhook → sync → webhook loops","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"jy_last_sync_id","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":") and ignore webhooks where it just changed.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Token refresh","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"expires_in - 60s","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", single-flight the refresh (only one worker refreshes; others wait on a lock).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search latency for fresh writes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Clock skew","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— Redis is authoritative. Don't use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"time()","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"from each worker for window scoring; use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"redis.call('TIME')","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"inside the Lua script.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Dead workers with held tokens","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The 5% error rule","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Phantom contention with Laravel","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"RateLimited","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"middleware","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":": it's per-job-class by default, not per-portal. If your","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"SyncDealJob","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"UpdateActivityJob","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"HubspotRateLimiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"service that everything routes through.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give positive feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give negative feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"You said: What will happen if there are two different types of rate limits?","depth":20,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"You said: What will happen if there are two different types of rate limits?","depth":21,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"What will happen if there are two different types of rate limits?","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search requests that have 5 requests per second","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"13:40","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Edit","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"Claude responded: They're independent buckets — with one shared bucket on top.","depth":21,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Claude responded: They're independent buckets — with one shared bucket on top.","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Analyzed independent rate limit buckets and their practical interactions","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Analyzed independent rate limit buckets and their practical interactions","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"They're independent buckets — with one shared bucket on top.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"The three buckets and what each call hits","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"The three buckets and what each call hits","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"┌──────────────────┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search request ──→ │ Search: 5/sec │ ──┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"└──────────────────┘ │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"├──→ ┌─────────────────┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"┌──────────────────┐ │ │ Daily: 250k–1M │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"└──────────────────┘ └─────────────────┘","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"What this means practically","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"What this means practically","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting search secondly limit (5/s):","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Only further","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/search","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting burst limit (190/10s):","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting daily limit:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"So can one type of request block the other?","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"So can one type of request block the other?","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search → Burst:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"No.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Burst → Search:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"No.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Saturating burst leaves Search's 5/s untouched.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Either → Daily:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Yes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", but only by contributing to the same daily counter. It's volume that kills you here, not the type.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"In theory you can sustain ~50 search + ~190 other =","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"~240 calls per 10 seconds","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"indefinitely (until daily runs out), because the two buckets bill independently.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Implementation consequence","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Implementation consequence","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"php","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'search'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// for search endpoints only","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'burst'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// for everything else","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'daily'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// every call, both kinds","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('search')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"+","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('daily')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". A batch update calls","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('burst')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"+","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('daily')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":".","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"One subtle pitfall: when you backoff on a 429,","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"inspect","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"policyName","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"in the response body","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to know which bucket actually fired. If you see","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"SECONDLY","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The other subtlety: the burst response gives you","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give positive feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give negative feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"You said: Ok, so what will happen each time I make a request?","depth":20,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"You said: Ok, so what will happen each time I make a request?","depth":21,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"13:44","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Edit","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.","depth":21,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Analyzed per-request overhead concerns and quantified Redis performance impact","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Analyzed per-request overhead concerns and quantified Redis performance impact","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out. But the overhead is much smaller than it sounds, and importantly, it's nowhere near your bottleneck.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"What \"two buckets per request\" actually costs","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"What \"two buckets per request\" actually costs","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Each request makes","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"one round trip to Redis","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— not two — because you fold both bucket checks into a single Lua script that runs atomically server-side:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"lua","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-- KEYS[1] = burst bucket key (or search)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-- KEYS[2] = daily bucket key","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-- ARGV[1] = now (ms) ARGV[2] = window_ms","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-- ARGV[3] = bucket_max ARGV[4] = daily_max","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-- ARGV[5] = request_id ARGV[6] = daily_ttl","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-- Trim sliding window","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"redis","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":".","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"call","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'ZREMRANGEBYSCORE'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"KEYS","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"[","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"]","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"0","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"ARGV","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"[","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"]","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"ARGV","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"[","depth":27,"on_screen":false,"role_description":"text"}]...
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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
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Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
Copy
Give positive feedback
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Retry
You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
Retry
Edit
Copy
Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
Copy to clipboard
php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.
Copy
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Retry
You said: Ok, so what will happen each time I make a request?
You said: Ok, so what will happen each time I make a request?
Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?
13:44
Retry
Edit
Copy
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Analyzed per-request overhead concerns and quantified Redis performance impact
Analyzed per-request overhead concerns and quantified Redis performance impact
Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out. But the overhead is much smaller than it sounds, and importantly, it's nowhere near your bottleneck.
What "two buckets per request" actually costs
What "two buckets per request" actually costs
Each request makes
one round trip to Redis
— not two — because you fold both bucket checks into a single Lua script that runs atomically server-side:
Copy to clipboard
lua
-- KEYS[1] = burst bucket key (or search)
-- KEYS[2] = daily bucket key
-- ARGV[1] = now (ms) ARGV[2] = window_ms
-- ARGV[3] = bucket_max ARGV[4] = daily_max
-- ARGV[5] = request_id ARGV[6] = daily_ttl
-- Trim sliding window
redis
.
call
(
'ZREMRANGEBYSCORE'
,
KEYS
[
1
]
,
0
,
ARGV
[
1
]
-
ARGV
[...
|
2554
|
NULL
|
NULL
|
NULL
|
|
2557
|
108
|
14
|
2026-05-07T11:26:05.688005+00:00
|
/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778153165688_m2.jpg...
|
Claude
|
Claude
|
True
|
NULL
|
monitor_2
|
NULL
|
NULL
|
NULL
|
NULL
|
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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
Show more
10:42
Retry
Edit
Copy
Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
Copy
Give positive feedback
Give negative feedback
Retry
You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
Retry
Edit
Copy
Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
Copy to clipboard
php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.
Copy
Give positive feedback
Give negative feedback
Retry
You said: Ok, so what will happen each time I make a request?
You said: Ok, so what will happen each time I make a request?
Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?
13:44
Retry
Edit
Copy
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Analyzed per-request overhead concerns and quantified Redis performance impact
Analyzed per-request overhead concerns and quantified Redis performance impact...
|
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Think carefully about the implementation and potential issue and bottlenecks.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Show more","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"10:42","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Edit","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"Claude responded: Current HubSpot limits (Apr 2026)","depth":21,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Claude responded: Current HubSpot limits (Apr 2026)","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Current HubSpot limits (Apr 2026)","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Current HubSpot limits (Apr 2026)","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". The burst window is per app; the daily quota is per portal.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". That's the limit the 429 in your example is hitting (","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"secondly limit","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"on","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/crm/v3/objects/contact/search","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"). It's a separate bucket from the 190/10s, and critically, search responses do not include the","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-*","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"headers","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— you can't read remaining capacity off the response, you have to track it yourself.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Non-search endpoints return","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Max","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", and","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Interval-Milliseconds","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". The daily headers are not returned for OAuth-authenticated calls","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— that's enforced for marketplace listing, but it's also a sane operational target.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"So you have","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"four buckets","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Where to store it","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Where to store it","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"INCR","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"EXPIRE","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"plus Lua scripting for true CAS semantics.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Key layout I'd use:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:search:{portalId} ZSET (sliding 1s)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:meta:{portalId} HASH (last seen headers)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The two short-window buckets are sliding-window-log (Redis ZSET, score =","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"microtime","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The daily bucket is a plain","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"INCR","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with TTL set to seconds-until-midnight in","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"HubSpot's account timezone","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to read it","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to read it","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Two-phase: optimistic pre-check, authoritative post-update.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Before a call, run the Lua acquire script. If it returns \"denied,\" sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"After the call, parse","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Max","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Interval-Milliseconds","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and store them in","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:meta:{portalId}","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"For search specifically, since headers don't come back, the local counter","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"is","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"not","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"on 429 (you really did make that request).","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to work with multiple jobs","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to work with multiple jobs","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Concurrency cap","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"via","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Queue::throttle()","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Per-tenant fairness","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Priority lanes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot-priority","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot-bulk","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Backoff on 429","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— respect the","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry-After","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"header. If absent, exponential with jitter (e.g.,","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"min(2^attempt * 250ms, 30s) ± 20%","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to work with paginated requests","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to work with paginated requests","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The trap is treating \"fetch all\" as one logical operation. Each page is its own API call and competes for tokens with everything else.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Two practical rules:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Don't hold the worker idle while paginating.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"processing","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"of page N as a separate job, and queue a \"fetch page N+1\" job with a delay equal to the wait time. Each page becomes its own atomic unit.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Always batch where HubSpot offers it.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/{type}/batch/{read|update|create}","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"accepts up to 100 IDs per call. For your \"patching IDs\" flow this is the difference between 600 calls and 6.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Walkthrough: 600 opportunities","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Walkthrough: 600 opportunities","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Sane flow:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Identify deltas","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— if you can avoid Search at all, do. Use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Batch read","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"current state —","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/deals/batch/read","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with 100 IDs per call → 6 calls. Burst weight 6.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Compute diff","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"in your code (no API).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Batch update","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"—","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/deals/batch/update","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with 100 per call → 6 calls.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Associations","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"—","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v4/associations/{from}/{to}/batch/create","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"if needed → another few batch calls.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Recognition: pre-check denies if","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"<weight>","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"slots aren't free; on 429 the response surfaces","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"policyName","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(DAILY/SECONDLY) — log it, push the job back with","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry-After","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"delay, increment a metric so you can alarm on sustained 429 rate.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Pitfalls & bottlenecks worth pre-empting","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Pitfalls & bottlenecks worth pre-empting","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Daily quota is shared across the whole portal","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search is uniquely fragile","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":": separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hs_object_id","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"ranges or by","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"windows.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry storms after HubSpot incidents","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Webhook → sync → webhook loops","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"jy_last_sync_id","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":") and ignore webhooks where it just changed.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Token refresh","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"expires_in - 60s","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", single-flight the refresh (only one worker refreshes; others wait on a lock).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search latency for fresh writes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Clock skew","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— Redis is authoritative. Don't use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"time()","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"from each worker for window scoring; use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"redis.call('TIME')","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"inside the Lua script.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Dead workers with held tokens","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The 5% error rule","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Phantom contention with Laravel","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"RateLimited","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"middleware","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":": it's per-job-class by default, not per-portal. If your","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"SyncDealJob","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"UpdateActivityJob","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"HubspotRateLimiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"service that everything routes through.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give positive feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give negative feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"You said: What will happen if there are two different types of rate limits?","depth":20,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"You said: What will happen if there are two different types of rate limits?","depth":21,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"What will happen if there are two different types of rate limits?","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search requests that have 5 requests per second","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"13:40","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Edit","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"Claude responded: They're independent buckets — with one shared bucket on top.","depth":21,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Claude responded: They're independent buckets — with one shared bucket on top.","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Analyzed independent rate limit buckets and their practical interactions","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Analyzed independent rate limit buckets and their practical interactions","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"They're independent buckets — with one shared bucket on top.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"The three buckets and what each call hits","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"The three buckets and what each call hits","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"┌──────────────────┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search request ──→ │ Search: 5/sec │ ──┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"└──────────────────┘ │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"├──→ ┌─────────────────┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"┌──────────────────┐ │ │ Daily: 250k–1M │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"└──────────────────┘ └─────────────────┘","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"What this means practically","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"What this means practically","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting search secondly limit (5/s):","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Only further","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/search","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting burst limit (190/10s):","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting daily limit:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"So can one type of request block the other?","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"So can one type of request block the other?","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search → Burst:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"No.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Burst → Search:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"No.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Saturating burst leaves Search's 5/s untouched.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Either → Daily:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Yes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", but only by contributing to the same daily counter. It's volume that kills you here, not the type.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"In theory you can sustain ~50 search + ~190 other =","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"~240 calls per 10 seconds","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"indefinitely (until daily runs out), because the two buckets bill independently.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Implementation consequence","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Implementation consequence","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"php","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'search'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// for search endpoints only","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'burst'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// for everything else","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'daily'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// every call, both kinds","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('search')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"+","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('daily')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". A batch update calls","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('burst')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"+","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('daily')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":".","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"One subtle pitfall: when you backoff on a 429,","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"inspect","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"policyName","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"in the response body","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to know which bucket actually fired. If you see","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"SECONDLY","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The other subtlety: the burst response gives you","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give positive feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give negative feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"You said: Ok, so what will happen each time I make a request?","depth":20,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"You said: Ok, so what will happen each time I make a request?","depth":21,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"13:44","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Edit","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.","depth":21,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Analyzed per-request overhead concerns and quantified Redis performance impact","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Analyzed per-request overhead concerns and quantified Redis performance impact","depth":23,"on_screen":false,"role_description":"text"}]...
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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
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10:42
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Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
Copy
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Retry
You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
Retry
Edit
Copy
Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
Copy to clipboard
php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.
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Retry
You said: Ok, so what will happen each time I make a request?
You said: Ok, so what will happen each time I make a request?
Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?
13:44
Retry
Edit
Copy
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Analyzed per-request overhead concerns and quantified Redis performance impact
Analyzed per-request overhead concerns and quantified Redis performance impact...
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/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778153168259_m2.jpg...
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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
Show more
10:42
Retry
Edit
Copy
Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
Copy
Give positive feedback
Give negative feedback
Retry
You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
Retry
Edit
Copy
Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
Copy to clipboard
php
$limiter
->
acquire
(
'search'...
|
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Think carefully about the implementation and potential issue and bottlenecks.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Show more","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"10:42","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Edit","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"Claude responded: Current HubSpot limits (Apr 2026)","depth":21,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Claude responded: Current HubSpot limits (Apr 2026)","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Current HubSpot limits (Apr 2026)","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Current HubSpot limits (Apr 2026)","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". The burst window is per app; the daily quota is per portal.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". That's the limit the 429 in your example is hitting (","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"secondly limit","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"on","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/crm/v3/objects/contact/search","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"). It's a separate bucket from the 190/10s, and critically, search responses do not include the","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-*","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"headers","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— you can't read remaining capacity off the response, you have to track it yourself.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Non-search endpoints return","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Max","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", and","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Interval-Milliseconds","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". The daily headers are not returned for OAuth-authenticated calls","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— that's enforced for marketplace listing, but it's also a sane operational target.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"So you have","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"four buckets","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Where to store it","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Where to store it","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"INCR","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"EXPIRE","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"plus Lua scripting for true CAS semantics.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Key layout I'd use:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:search:{portalId} ZSET (sliding 1s)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:meta:{portalId} HASH (last seen headers)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The two short-window buckets are sliding-window-log (Redis ZSET, score =","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"microtime","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The daily bucket is a plain","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"INCR","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with TTL set to seconds-until-midnight in","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"HubSpot's account timezone","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to read it","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to read it","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Two-phase: optimistic pre-check, authoritative post-update.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Before a call, run the Lua acquire script. If it returns \"denied,\" sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"After the call, parse","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Max","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Interval-Milliseconds","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and store them in","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:meta:{portalId}","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"For search specifically, since headers don't come back, the local counter","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"is","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"not","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"on 429 (you really did make that request).","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to work with multiple jobs","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to work with multiple jobs","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Concurrency cap","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"via","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Queue::throttle()","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Per-tenant fairness","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Priority lanes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot-priority","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot-bulk","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Backoff on 429","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— respect the","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry-After","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"header. If absent, exponential with jitter (e.g.,","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"min(2^attempt * 250ms, 30s) ± 20%","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to work with paginated requests","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to work with paginated requests","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The trap is treating \"fetch all\" as one logical operation. Each page is its own API call and competes for tokens with everything else.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Two practical rules:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Don't hold the worker idle while paginating.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"processing","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"of page N as a separate job, and queue a \"fetch page N+1\" job with a delay equal to the wait time. Each page becomes its own atomic unit.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Always batch where HubSpot offers it.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/{type}/batch/{read|update|create}","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"accepts up to 100 IDs per call. For your \"patching IDs\" flow this is the difference between 600 calls and 6.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Walkthrough: 600 opportunities","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Walkthrough: 600 opportunities","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Sane flow:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Identify deltas","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— if you can avoid Search at all, do. Use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Batch read","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"current state —","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/deals/batch/read","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with 100 IDs per call → 6 calls. Burst weight 6.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Compute diff","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"in your code (no API).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Batch update","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"—","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/deals/batch/update","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with 100 per call → 6 calls.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Associations","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"—","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v4/associations/{from}/{to}/batch/create","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"if needed → another few batch calls.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Recognition: pre-check denies if","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"<weight>","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"slots aren't free; on 429 the response surfaces","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"policyName","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(DAILY/SECONDLY) — log it, push the job back with","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry-After","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"delay, increment a metric so you can alarm on sustained 429 rate.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Pitfalls & bottlenecks worth pre-empting","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Pitfalls & bottlenecks worth pre-empting","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Daily quota is shared across the whole portal","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search is uniquely fragile","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":": separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hs_object_id","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"ranges or by","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"windows.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry storms after HubSpot incidents","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Webhook → sync → webhook loops","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"jy_last_sync_id","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":") and ignore webhooks where it just changed.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Token refresh","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"expires_in - 60s","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", single-flight the refresh (only one worker refreshes; others wait on a lock).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search latency for fresh writes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Clock skew","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— Redis is authoritative. Don't use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"time()","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"from each worker for window scoring; use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"redis.call('TIME')","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"inside the Lua script.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Dead workers with held tokens","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The 5% error rule","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Phantom contention with Laravel","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"RateLimited","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"middleware","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":": it's per-job-class by default, not per-portal. If your","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"SyncDealJob","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"UpdateActivityJob","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"HubspotRateLimiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"service that everything routes through.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give positive feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give negative feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"You said: What will happen if there are two different types of rate limits?","depth":20,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"You said: What will happen if there are two different types of rate limits?","depth":21,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"What will happen if there are two different types of rate limits?","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search requests that have 5 requests per second","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"13:40","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Edit","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"Claude responded: They're independent buckets — with one shared bucket on top.","depth":21,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Claude responded: They're independent buckets — with one shared bucket on top.","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Analyzed independent rate limit buckets and their practical interactions","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Analyzed independent rate limit buckets and their practical interactions","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"They're independent buckets — with one shared bucket on top.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"The three buckets and what each call hits","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"The three buckets and what each call hits","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"┌──────────────────┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search request ──→ │ Search: 5/sec │ ──┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"└──────────────────┘ │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"├──→ ┌─────────────────┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"┌──────────────────┐ │ │ Daily: 250k–1M │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"└──────────────────┘ └─────────────────┘","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"What this means practically","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"What this means practically","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting search secondly limit (5/s):","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Only further","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/search","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting burst limit (190/10s):","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting daily limit:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"So can one type of request block the other?","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"So can one type of request block the other?","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search → Burst:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"No.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Burst → Search:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"No.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Saturating burst leaves Search's 5/s untouched.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Either → Daily:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Yes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", but only by contributing to the same daily counter. It's volume that kills you here, not the type.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"In theory you can sustain ~50 search + ~190 other =","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"~240 calls per 10 seconds","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"indefinitely (until daily runs out), because the two buckets bill independently.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Implementation consequence","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Implementation consequence","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"php","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'search'","depth":27,"on_screen":false,"role_description":"text"}]...
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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
Show more
10:42
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Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
Copy
Give positive feedback
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Retry
You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
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Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
Copy to clipboard
php
$limiter
->
acquire
(
'search'...
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2026-05-07T11:26:38.847328+00:00
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/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778153198847_m2.jpg...
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Claude
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Claude
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Claude is responding
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
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10:42
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Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
Copy
Give positive feedback
Give negative feedback
You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
Edit
Copy
Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
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php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.
Copy
Give positive feedback
Give negative feedback
You said: Ok, so what will happen each time I make a request?
You said: Ok, so what will happen each time I make a request?
Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?
13:44
Edit
Copy
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Analyzed per-request overhead concerns and quantified Redis performance impact
Analyzed per-request overhead concerns and quantified Redis performance impact
Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out. But the overhead is much smaller than it sounds, and importantly, it's nowhere near your bottleneck.
What "two buckets per request" actually costs
What "two buckets per request" actually costs
Each request makes
one round trip to Redis
— not two — because you fold both bucket checks into a single Lua script that runs atomically server-side:
Copy to clipboard
lua
-- KEYS[1] = burst bucket key (or search)
-- KEYS[2] = daily bucket key
-- ARGV[1] = now (ms) ARGV[2] = window_ms
-- ARGV[3] = bucket_max ARGV[4] = daily_max
-- ARGV[5] = request_id ARGV[6] = daily_ttl
-- Trim sliding window
redis
.
call
(
'ZREMRANGEBYSCORE'
,
KEYS
[
1
]
,
0
,
ARGV
[
1
]
-
ARGV
[
2
]
)
local
burst_used
=
redis
.
call
(
'ZCARD'
,
KEYS
[
1
]
)
local
daily_used
=
tonumber
(
redis
.
call
(
'GET'
,
KEYS
[
2
]
)
or
'0'
)
if
burst_used
>=
tonumber
(
ARGV
[
3
]
)
then
-- Tell caller how long to sleep until oldest entry expires
local
oldest
=
redis
.
call
(
'ZRANGE'
,
KEYS
[
1
]
,
0
,
0
,
'WITHSCORES'
)
return
{
0
,
'BURST'
,
(
oldest
[
2
]
+
ARGV
[
2
]
)
-
ARGV
[
1
]
}
end
if
daily_used
>=
tonumber
(
ARGV
[
4
]
)
then
return
{
0
,
'DAILY'
,
-
1
}
end
redis
.
call
(
'ZADD'
,
KEYS
[
1
]
,
ARGV
[
1
]
,
ARGV
[
5
]
)
redis
.
call
(
'PEXPIRE'
,
KEYS
[
1
]
,
ARGV
[
2
]
+
1000
)
local
d
=
redis
.
call
(
'INCR'
,
KEYS
[
2
]
)
if
d
==
1...
|
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The daily headers are not returned for OAuth-authenticated calls","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— that's enforced for marketplace listing, but it's also a sane operational target.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"So you have","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"four buckets","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Where to store it","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Where to store it","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"INCR","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"EXPIRE","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"plus Lua scripting for true CAS semantics.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Key layout I'd use:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:search:{portalId} ZSET (sliding 1s)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:meta:{portalId} HASH (last seen headers)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The two short-window buckets are sliding-window-log (Redis ZSET, score =","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"microtime","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The daily bucket is a plain","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"INCR","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with TTL set to seconds-until-midnight in","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"HubSpot's account timezone","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to read it","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to read it","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Two-phase: optimistic pre-check, authoritative post-update.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Before a call, run the Lua acquire script. If it returns \"denied,\" sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"After the call, parse","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Max","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Interval-Milliseconds","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and store them in","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:meta:{portalId}","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"For search specifically, since headers don't come back, the local counter","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"is","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"not","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"on 429 (you really did make that request).","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to work with multiple jobs","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to work with multiple jobs","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Concurrency cap","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"via","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Queue::throttle()","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Per-tenant fairness","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Priority lanes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot-priority","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot-bulk","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Backoff on 429","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— respect the","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry-After","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"header. If absent, exponential with jitter (e.g.,","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"min(2^attempt * 250ms, 30s) ± 20%","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to work with paginated requests","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to work with paginated requests","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The trap is treating \"fetch all\" as one logical operation. Each page is its own API call and competes for tokens with everything else.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Two practical rules:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Don't hold the worker idle while paginating.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"processing","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"of page N as a separate job, and queue a \"fetch page N+1\" job with a delay equal to the wait time. Each page becomes its own atomic unit.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Always batch where HubSpot offers it.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/{type}/batch/{read|update|create}","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"accepts up to 100 IDs per call. For your \"patching IDs\" flow this is the difference between 600 calls and 6.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Walkthrough: 600 opportunities","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Walkthrough: 600 opportunities","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Sane flow:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Identify deltas","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— if you can avoid Search at all, do. Use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Batch read","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"current state —","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/deals/batch/read","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with 100 IDs per call → 6 calls. Burst weight 6.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Compute diff","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"in your code (no API).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Batch update","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"—","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/deals/batch/update","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with 100 per call → 6 calls.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Associations","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"—","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v4/associations/{from}/{to}/batch/create","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"if needed → another few batch calls.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Recognition: pre-check denies if","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"<weight>","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"slots aren't free; on 429 the response surfaces","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"policyName","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(DAILY/SECONDLY) — log it, push the job back with","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry-After","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"delay, increment a metric so you can alarm on sustained 429 rate.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Pitfalls & bottlenecks worth pre-empting","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Pitfalls & bottlenecks worth pre-empting","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Daily quota is shared across the whole portal","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search is uniquely fragile","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":": separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hs_object_id","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"ranges or by","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"windows.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry storms after HubSpot incidents","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Webhook → sync → webhook loops","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"jy_last_sync_id","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":") and ignore webhooks where it just changed.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Token refresh","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"expires_in - 60s","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", single-flight the refresh (only one worker refreshes; others wait on a lock).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search latency for fresh writes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Clock skew","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— Redis is authoritative. Don't use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"time()","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"from each worker for window scoring; use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"redis.call('TIME')","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"inside the Lua script.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Dead workers with held tokens","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The 5% error rule","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Phantom contention with Laravel","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"RateLimited","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"middleware","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":": it's per-job-class by default, not per-portal. If your","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"SyncDealJob","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"UpdateActivityJob","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"HubspotRateLimiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"service that everything routes through.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give positive feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give negative feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"You said: What will happen if there are two different types of rate limits?","depth":20,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"You said: What will happen if there are two different types of rate limits?","depth":21,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"What will happen if there are two different types of rate limits?","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search requests that have 5 requests per second","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"13:40","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Edit","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"Claude responded: They're independent buckets — with one shared bucket on top.","depth":21,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Claude responded: They're independent buckets — with one shared bucket on top.","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Analyzed independent rate limit buckets and their practical interactions","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Analyzed independent rate limit buckets and their practical interactions","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"They're independent buckets — with one shared bucket on top.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"The three buckets and what each call hits","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"The three buckets and what each call hits","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"┌──────────────────┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search request ──→ │ Search: 5/sec │ ──┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"└──────────────────┘ │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"├──→ ┌─────────────────┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"┌──────────────────┐ │ │ Daily: 250k–1M │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"└──────────────────┘ └─────────────────┘","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"What this means practically","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"What this means practically","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting search secondly limit (5/s):","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Only further","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/search","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. 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Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? 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HubSpot rate limit implementation strategy, rename chat
HubSpot rate limit implementation strategy
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Claude is responding
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
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Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
Copy
Give positive feedback
Give negative feedback
You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
Edit
Copy
Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
Copy to clipboard
php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.
Copy
Give positive feedback
Give negative feedback
You said: Ok, so what will happen each time I make a request?
You said: Ok, so what will happen each time I make a request?
Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?
13:44
Edit
Copy
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Analyzed per-request overhead concerns and quantified Redis performance impact
Analyzed per-request overhead concerns and quantified Redis performance impact
Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out. But the overhead is much smaller than it sounds, and importantly, it's nowhere near your bottleneck.
What "two buckets per request" actually costs
What "two buckets per request" actually costs
Each request makes
one round trip to Redis
— not two — because you fold both bucket checks into a single Lua script that runs atomically server-side:
Copy to clipboard
lua
-- KEYS[1] = burst bucket key (or search)
-- KEYS[2] = daily bucket key
-- ARGV[1] = now (ms) ARGV[2] = window_ms
-- ARGV[3] = bucket_max ARGV[4] = daily_max
-- ARGV[5] = request_id ARGV[6] = daily_ttl
-- Trim sliding window
redis
.
call
(
'ZREMRANGEBYSCORE'
,
KEYS
[
1
]
,
0
,
ARGV
[
1
]
-
ARGV
[
2
]
)
local
burst_used
=
redis
.
call
(
'ZCARD'
,
KEYS
[
1
]
)
local
daily_used
=
tonumber
(
redis
.
call
(
'GET'
,
KEYS
[
2
]
)
or
'0'
)
if
burst_used
>=
tonumber
(
ARGV
[
3
]
)
then
-- Tell caller how long to sleep until oldest entry expires
local
oldest
=
redis
.
call
(
'ZRANGE'
,
KEYS
[
1
]
,
0
,
0
,
'WITHSCORES'
)
return
{
0
,
'BURST'
,
(
oldest
[
2
]
+
ARGV
[
2
]
)
-
ARGV
[
1
]
}
end
if
daily_used
>=
tonumber
(
ARGV
[
4
]
)
then
return
{
0
,
'DAILY'
,
-
1
}
end
redis
.
call
(
'ZADD'
,
KEYS
[
1
]
,
ARGV
[
1
]
,
ARGV
[
5
]
)
redis
.
call
(
'PEXPIRE'
,
KEYS
[
1
]
,
ARGV
[
2
]
+
1000
)
local
d
=
redis
.
call
(
'INCR'
,
KEYS
[
2
]
)
if
d
==
1...
|
NULL
|
NULL
|
NULL
|
NULL
|
|
2563
|
108
|
17
|
2026-05-07T11:27:09.291443+00:00
|
/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778153229291_m2.jpg...
|
Claude
|
Claude
|
True
|
NULL
|
monitor_2
|
NULL
|
NULL
|
NULL
|
NULL
|
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Open sidebar
Claude is responding
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
Show more
10:42
Edit
Copy
Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
Copy
Give positive feedback
Give negative feedback
You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
Edit
Copy
Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
Copy to clipboard
php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining...
|
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Think carefully about the implementation and potential issue and bottlenecks.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Show more","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"10:42","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Edit","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"Claude responded: Current HubSpot limits (Apr 2026)","depth":21,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Claude responded: Current HubSpot limits (Apr 2026)","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Current HubSpot limits (Apr 2026)","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Current HubSpot limits (Apr 2026)","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". The burst window is per app; the daily quota is per portal.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". That's the limit the 429 in your example is hitting (","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"secondly limit","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"on","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/crm/v3/objects/contact/search","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"). It's a separate bucket from the 190/10s, and critically, search responses do not include the","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-*","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"headers","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— you can't read remaining capacity off the response, you have to track it yourself.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Non-search endpoints return","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Max","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", and","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Interval-Milliseconds","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". The daily headers are not returned for OAuth-authenticated calls","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— that's enforced for marketplace listing, but it's also a sane operational target.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"So you have","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"four buckets","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Where to store it","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Where to store it","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"INCR","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"EXPIRE","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"plus Lua scripting for true CAS semantics.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Key layout I'd use:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:search:{portalId} ZSET (sliding 1s)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:meta:{portalId} HASH (last seen headers)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The two short-window buckets are sliding-window-log (Redis ZSET, score =","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"microtime","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The daily bucket is a plain","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"INCR","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with TTL set to seconds-until-midnight in","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"HubSpot's account timezone","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to read it","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to read it","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Two-phase: optimistic pre-check, authoritative post-update.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Before a call, run the Lua acquire script. If it returns \"denied,\" sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"After the call, parse","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Max","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Interval-Milliseconds","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and store them in","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:meta:{portalId}","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"For search specifically, since headers don't come back, the local counter","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"is","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"not","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"on 429 (you really did make that request).","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to work with multiple jobs","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to work with multiple jobs","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Concurrency cap","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"via","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Queue::throttle()","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Per-tenant fairness","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Priority lanes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot-priority","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot-bulk","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Backoff on 429","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— respect the","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry-After","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"header. If absent, exponential with jitter (e.g.,","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"min(2^attempt * 250ms, 30s) ± 20%","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to work with paginated requests","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to work with paginated requests","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The trap is treating \"fetch all\" as one logical operation. Each page is its own API call and competes for tokens with everything else.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Two practical rules:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Don't hold the worker idle while paginating.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"processing","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"of page N as a separate job, and queue a \"fetch page N+1\" job with a delay equal to the wait time. Each page becomes its own atomic unit.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Always batch where HubSpot offers it.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/{type}/batch/{read|update|create}","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"accepts up to 100 IDs per call. For your \"patching IDs\" flow this is the difference between 600 calls and 6.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Walkthrough: 600 opportunities","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Walkthrough: 600 opportunities","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Sane flow:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Identify deltas","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— if you can avoid Search at all, do. Use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Batch read","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"current state —","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/deals/batch/read","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with 100 IDs per call → 6 calls. Burst weight 6.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Compute diff","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"in your code (no API).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Batch update","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"—","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/deals/batch/update","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with 100 per call → 6 calls.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Associations","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"—","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v4/associations/{from}/{to}/batch/create","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"if needed → another few batch calls.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Recognition: pre-check denies if","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"<weight>","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"slots aren't free; on 429 the response surfaces","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"policyName","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(DAILY/SECONDLY) — log it, push the job back with","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry-After","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"delay, increment a metric so you can alarm on sustained 429 rate.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Pitfalls & bottlenecks worth pre-empting","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Pitfalls & bottlenecks worth pre-empting","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Daily quota is shared across the whole portal","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search is uniquely fragile","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":": separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hs_object_id","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"ranges or by","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"windows.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry storms after HubSpot incidents","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Webhook → sync → webhook loops","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"jy_last_sync_id","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":") and ignore webhooks where it just changed.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Token refresh","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"expires_in - 60s","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", single-flight the refresh (only one worker refreshes; others wait on a lock).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search latency for fresh writes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Clock skew","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— Redis is authoritative. Don't use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"time()","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"from each worker for window scoring; use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"redis.call('TIME')","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"inside the Lua script.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Dead workers with held tokens","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The 5% error rule","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Phantom contention with Laravel","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"RateLimited","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"middleware","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":": it's per-job-class by default, not per-portal. If your","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"SyncDealJob","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"UpdateActivityJob","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"HubspotRateLimiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"service that everything routes through.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give positive feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give negative feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"You said: What will happen if there are two different types of rate limits?","depth":20,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"You said: What will happen if there are two different types of rate limits?","depth":21,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"What will happen if there are two different types of rate limits?","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search requests that have 5 requests per second","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"13:40","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Edit","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"Claude responded: They're independent buckets — with one shared bucket on top.","depth":21,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Claude responded: They're independent buckets — with one shared bucket on top.","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Analyzed independent rate limit buckets and their practical interactions","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Analyzed independent rate limit buckets and their practical interactions","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"They're independent buckets — with one shared bucket on top.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"The three buckets and what each call hits","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"The three buckets and what each call hits","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"┌──────────────────┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search request ──→ │ Search: 5/sec │ ──┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"└──────────────────┘ │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"├──→ ┌─────────────────┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"┌──────────────────┐ │ │ Daily: 250k–1M │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"└──────────────────┘ └─────────────────┘","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"What this means practically","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"What this means practically","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting search secondly limit (5/s):","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Only further","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/search","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting burst limit (190/10s):","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting daily limit:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"So can one type of request block the other?","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"So can one type of request block the other?","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search → Burst:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"No.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Burst → Search:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"No.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Saturating burst leaves Search's 5/s untouched.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Either → Daily:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Yes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", but only by contributing to the same daily counter. It's volume that kills you here, not the type.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"In theory you can sustain ~50 search + ~190 other =","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"~240 calls per 10 seconds","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"indefinitely (until daily runs out), because the two buckets bill independently.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Implementation consequence","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Implementation consequence","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"php","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'search'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// for search endpoints only","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'burst'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// for everything else","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'daily'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// every call, both kinds","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('search')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"+","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('daily')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". A batch update calls","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('burst')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"+","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('daily')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":".","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"One subtle pitfall: when you backoff on a 429,","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"inspect","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"policyName","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"in the response body","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to know which bucket actually fired. If you see","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"SECONDLY","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The other subtlety: the burst response gives you","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":26,"on_screen":false,"role_description":"text"}]...
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Claude is responding
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
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Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
Copy
Give positive feedback
Give negative feedback
You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
Edit
Copy
Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
Copy to clipboard
php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining...
|
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18
|
2026-05-07T11:27:13.257799+00:00
|
/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778153233257_m2.jpg...
|
Claude
|
Claude
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Open sidebar
Claude is responding
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
Show more
10:42
Edit
Copy
Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
Copy
Give positive feedback
Give negative feedback
You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
Edit
Copy
Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
Copy to clipboard
php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.
Copy
Give positive feedback
Give negative feedback
You said: Ok, so what will happen each time I make a request?
You said: Ok, so what will happen each time I make a request?
Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?
13:44
Edit
Copy
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Analyzed per-request overhead concerns and quantified Redis performance impact
Analyzed per-request overhead concerns and quantified Redis performance impact
Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out. But the overhead is much smaller than it sounds, and importantly, it's nowhere near your bottleneck.
What "two buckets per request" actually costs
What "two buckets per request" actually costs
Each request makes
one round trip to Redis
— not two — because you fold both bucket checks into a single Lua script that runs atomically server-side:
Copy to clipboard
lua
-- KEYS[1] = burst bucket key (or search)
-- KEYS[2] = daily bucket key
-- ARGV[1] = now (ms) ARGV[2] = window_ms
-- ARGV[3] = bucket_max ARGV[4] = daily_max
-- ARGV[5] = request_id ARGV[6] = daily_ttl
-- Trim sliding window
redis
.
call
(
'ZREMRANGEBYSCORE'
,
KEYS
[
1
]
,
0
,
ARGV
[
1
]
-
ARGV
[
2
]
)
local
burst_used
=
redis
.
call
(
'ZCARD'
,
KEYS
[
1
]
)
local
daily_used
=
tonumber
(
redis
.
call
(
'GET'
,
KEYS
[
2
]
)
or
'0'
)
if
burst_used
>=
tonumber
(
ARGV
[
3
]
)
then
-- Tell caller how long to sleep until oldest entry expires
local
oldest
=
redis
.
call
(
'ZRANGE'
,
KEYS
[
1
]
,
0
,
0
,
'WITHSCORES'
)
return
{
0
,
'BURST'
,
(
oldest
[
2
]
+
ARGV
[
2
]...
|
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Think carefully about the implementation and potential issue and bottlenecks.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Show more","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"10:42","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Edit","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"Claude responded: Current HubSpot limits (Apr 2026)","depth":21,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Claude responded: Current HubSpot limits (Apr 2026)","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Current HubSpot limits (Apr 2026)","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Current HubSpot limits (Apr 2026)","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". The burst window is per app; the daily quota is per portal.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". That's the limit the 429 in your example is hitting (","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"secondly limit","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"on","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/crm/v3/objects/contact/search","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"). It's a separate bucket from the 190/10s, and critically, search responses do not include the","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-*","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"headers","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— you can't read remaining capacity off the response, you have to track it yourself.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Non-search endpoints return","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Max","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", and","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Interval-Milliseconds","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". The daily headers are not returned for OAuth-authenticated calls","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— that's enforced for marketplace listing, but it's also a sane operational target.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"So you have","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"four buckets","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Where to store it","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Where to store it","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"INCR","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"EXPIRE","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"plus Lua scripting for true CAS semantics.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Key layout I'd use:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:search:{portalId} ZSET (sliding 1s)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:meta:{portalId} HASH (last seen headers)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The two short-window buckets are sliding-window-log (Redis ZSET, score =","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"microtime","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The daily bucket is a plain","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"INCR","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with TTL set to seconds-until-midnight in","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"HubSpot's account timezone","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to read it","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to read it","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Two-phase: optimistic pre-check, authoritative post-update.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Before a call, run the Lua acquire script. If it returns \"denied,\" sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"After the call, parse","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Max","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Interval-Milliseconds","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and store them in","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:meta:{portalId}","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"For search specifically, since headers don't come back, the local counter","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"is","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"not","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"on 429 (you really did make that request).","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to work with multiple jobs","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to work with multiple jobs","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Concurrency cap","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"via","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Queue::throttle()","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Per-tenant fairness","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Priority lanes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot-priority","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot-bulk","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Backoff on 429","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— respect the","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry-After","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"header. If absent, exponential with jitter (e.g.,","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"min(2^attempt * 250ms, 30s) ± 20%","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to work with paginated requests","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to work with paginated requests","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The trap is treating \"fetch all\" as one logical operation. Each page is its own API call and competes for tokens with everything else.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Two practical rules:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Don't hold the worker idle while paginating.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"processing","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"of page N as a separate job, and queue a \"fetch page N+1\" job with a delay equal to the wait time. Each page becomes its own atomic unit.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Always batch where HubSpot offers it.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/{type}/batch/{read|update|create}","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"accepts up to 100 IDs per call. For your \"patching IDs\" flow this is the difference between 600 calls and 6.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Walkthrough: 600 opportunities","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Walkthrough: 600 opportunities","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Sane flow:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Identify deltas","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— if you can avoid Search at all, do. Use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Batch read","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"current state —","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/deals/batch/read","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with 100 IDs per call → 6 calls. Burst weight 6.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Compute diff","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"in your code (no API).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Batch update","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"—","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/deals/batch/update","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with 100 per call → 6 calls.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Associations","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"—","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v4/associations/{from}/{to}/batch/create","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"if needed → another few batch calls.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Recognition: pre-check denies if","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"<weight>","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"slots aren't free; on 429 the response surfaces","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"policyName","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(DAILY/SECONDLY) — log it, push the job back with","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry-After","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"delay, increment a metric so you can alarm on sustained 429 rate.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Pitfalls & bottlenecks worth pre-empting","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Pitfalls & bottlenecks worth pre-empting","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Daily quota is shared across the whole portal","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search is uniquely fragile","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":": separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hs_object_id","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"ranges or by","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"windows.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry storms after HubSpot incidents","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Webhook → sync → webhook loops","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"jy_last_sync_id","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":") and ignore webhooks where it just changed.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Token refresh","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"expires_in - 60s","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", single-flight the refresh (only one worker refreshes; others wait on a lock).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search latency for fresh writes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Clock skew","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— Redis is authoritative. Don't use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"time()","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"from each worker for window scoring; use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"redis.call('TIME')","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"inside the Lua script.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Dead workers with held tokens","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The 5% error rule","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Phantom contention with Laravel","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"RateLimited","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"middleware","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":": it's per-job-class by default, not per-portal. 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Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"13:44","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Edit","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.","depth":21,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Analyzed per-request overhead concerns and quantified Redis performance impact","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Analyzed per-request overhead concerns and quantified Redis performance impact","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out. 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daily_max","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-- ARGV[5] = request_id ARGV[6] = daily_ttl","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-- Trim sliding 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n":"text"},{"role":"AXStaticText","text":"KEYS","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"[","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"2","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"]","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"or","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'0'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"if","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"burst_used","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":">=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"tonumber","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"ARGV","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"[","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"3","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"]","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"then","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-- Tell caller how long to sleep until oldest entry expires","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"local","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"oldest","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"redis","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":".","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"call","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'ZRANGE'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"KEYS","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"[","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"]","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"0","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"0","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'WITHSCORES'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"return","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"{","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"0","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'BURST'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"oldest","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"[","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"2","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"]","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"+","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"ARGV","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"[","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"2","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"]","depth":27,"on_screen":false,"role_description":"text"}]...
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Claude is responding
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
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10:42
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Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
Copy
Give positive feedback
Give negative feedback
You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
Edit
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Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
Copy to clipboard
php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.
Copy
Give positive feedback
Give negative feedback
You said: Ok, so what will happen each time I make a request?
You said: Ok, so what will happen each time I make a request?
Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?
13:44
Edit
Copy
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Analyzed per-request overhead concerns and quantified Redis performance impact
Analyzed per-request overhead concerns and quantified Redis performance impact
Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out. But the overhead is much smaller than it sounds, and importantly, it's nowhere near your bottleneck.
What "two buckets per request" actually costs
What "two buckets per request" actually costs
Each request makes
one round trip to Redis
— not two — because you fold both bucket checks into a single Lua script that runs atomically server-side:
Copy to clipboard
lua
-- KEYS[1] = burst bucket key (or search)
-- KEYS[2] = daily bucket key
-- ARGV[1] = now (ms) ARGV[2] = window_ms
-- ARGV[3] = bucket_max ARGV[4] = daily_max
-- ARGV[5] = request_id ARGV[6] = daily_ttl
-- Trim sliding window
redis
.
call
(
'ZREMRANGEBYSCORE'
,
KEYS
[
1
]
,
0
,
ARGV
[
1
]
-
ARGV
[
2
]
)
local
burst_used
=
redis
.
call
(
'ZCARD'
,
KEYS
[
1
]
)
local
daily_used
=
tonumber
(
redis
.
call
(
'GET'
,
KEYS
[
2
]
)
or
'0'
)
if
burst_used
>=
tonumber
(
ARGV
[
3
]
)
then
-- Tell caller how long to sleep until oldest entry expires
local
oldest
=
redis
.
call
(
'ZRANGE'
,
KEYS
[
1
]
,
0
,
0
,
'WITHSCORES'
)
return
{
0
,
'BURST'
,
(
oldest
[
2
]
+
ARGV
[
2
]...
|
NULL
|
NULL
|
NULL
|
NULL
|
|
2566
|
108
|
19
|
2026-05-07T11:27:43.777862+00:00
|
/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778153263777_m2.jpg...
|
Claude
|
Claude
|
True
|
NULL
|
monitor_2
|
NULL
|
NULL
|
NULL
|
NULL
|
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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
Show more
10:42
Retry
Edit
Copy
Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
Copy
Give positive feedback
Give negative feedback
Retry
You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
Retry
Edit
Copy
Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
Copy to clipboard
php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.
Copy
Give positive feedback
Give negative feedback
Retry
You said: Ok, so what will happen each time I make a request?
You said: Ok, so what will happen each time I make a request?
Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?
13:44
Retry
Edit
Copy
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Analyzed per-request overhead concerns and quantified Redis performance impact
Analyzed per-request overhead concerns and quantified Redis performance impact
Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out. But the overhead is much smaller than it sounds, and importantly, it's nowhere near your bottleneck.
What "two buckets per request" actually costs
What "two buckets per request" actually costs
Each request makes
one round trip to Redis
— not two — because you fold both bucket checks into a single Lua script that runs atomically server-side:
Copy to clipboard
lua
-- KEYS[1] = burst bucket key (or search)
-- KEYS[2] = daily bucket key
-- ARGV[1] = now (ms) ARGV[2] = window_ms
-- ARGV[3] = bucket_max ARGV[4] = daily_max
-- ARGV[5] = request_id ARGV[6] = daily_ttl
-- Trim sliding window
redis
.
call
(
'ZREMRANGEBYSCORE'
,
KEYS
[
1
]
,
0
,
ARGV
[
1
]
-
ARGV
[
2
]
)
local
burst_used
=
redis
.
call
(
'ZCARD'
,
KEYS
[
1
]
)
local
daily_used
=
tonumber
(
redis
.
call
(
'GET'
,
KEYS
[
2
]
)
or
'0'
)
if
burst_used
>=
tonumber
(
ARGV
[
3
]
)
then
-- Tell caller how long to sleep until oldest entry expires
local
oldest
=
redis
.
call
(
'ZRANGE'
,
KEYS
[
1
]
,
0
,
0
,
'WITHSCORES'
)
return
{
0
,
'BURST'
,
(
oldest
[
2
]
+
ARGV
[
2
]
)
-
ARGV
[
1
]
}
end
if
daily_used
>=
tonumber
(
ARGV
[
4
]
)
then
return
{
0
,
'DAILY'
,
-
1
}
end
redis
.
call
(
'ZADD'
,
KEYS
[
1
]
,
ARGV
[
1
]
,
ARGV
[
5
]
)
redis
.
call
(
'PEXPIRE'
,
KEYS
[
1
]
,
ARGV
[
2
]
+
1000
)
local
d
=
redis
.
call
(
'INCR'
,
KEYS
[
2
]
)
if
d
==
1
then
redis
.
call
(
'EXPIRE'
,
KEYS
[
2
]
,
ARGV
[
6
]
)
end
return
{
1
,
'OK'
,
tonumber
(
ARGV
[
3
]
)
-
burst_used
-
1
}
One
EVALSHA
call. Two keys touched. Returns either
{1, OK, remaining}
or
{0, reason, retry_ms}
. No race conditions because Lua is single-threaded inside Redis. No "check then increment" gap that other workers can sneak through.
The math on whether this is heavy
The math on whether this is heavy
For your 100,000 requests, with 100 PATCHes batched into 1 batch_update call per 100 deals, you're realistically making more like
1,000–2,000 actual API calls
(assuming you're using
/batch/update
and
/batch/read
properly). But let's pretend you really make all 100k.
At HubSpot's ceiling of ~240 calls/sec across both buckets, 100k calls take roughly
7 minutes
of wall time. That means:...
|
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Think carefully about the implementation and potential issue and bottlenecks.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Show more","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"10:42","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Edit","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"Claude responded: Current HubSpot limits (Apr 2026)","depth":21,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Claude responded: Current HubSpot limits (Apr 2026)","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Current HubSpot limits (Apr 2026)","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Current HubSpot limits (Apr 2026)","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". The burst window is per app; the daily quota is per portal.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". That's the limit the 429 in your example is hitting (","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"secondly limit","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"on","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/crm/v3/objects/contact/search","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"). It's a separate bucket from the 190/10s, and critically, search responses do not include the","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-*","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"headers","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— you can't read remaining capacity off the response, you have to track it yourself.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Non-search endpoints return","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Max","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", and","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Interval-Milliseconds","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". The daily headers are not returned for OAuth-authenticated calls","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— that's enforced for marketplace listing, but it's also a sane operational target.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"So you have","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"four buckets","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Where to store it","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Where to store it","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"INCR","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"EXPIRE","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"plus Lua scripting for true CAS semantics.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Key layout I'd use:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:search:{portalId} ZSET (sliding 1s)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:meta:{portalId} HASH (last seen headers)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The two short-window buckets are sliding-window-log (Redis ZSET, score =","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"microtime","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The daily bucket is a plain","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"INCR","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with TTL set to seconds-until-midnight in","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"HubSpot's account timezone","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to read it","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to read it","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Two-phase: optimistic pre-check, authoritative post-update.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Before a call, run the Lua acquire script. If it returns \"denied,\" sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"After the call, parse","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Max","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Interval-Milliseconds","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and store them in","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:meta:{portalId}","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"For search specifically, since headers don't come back, the local counter","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"is","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"not","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"on 429 (you really did make that request).","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to work with multiple jobs","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to work with multiple jobs","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Concurrency cap","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"via","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Queue::throttle()","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Per-tenant fairness","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Priority lanes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot-priority","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot-bulk","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Backoff on 429","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— respect the","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry-After","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"header. If absent, exponential with jitter (e.g.,","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"min(2^attempt * 250ms, 30s) ± 20%","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to work with paginated requests","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to work with paginated requests","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The trap is treating \"fetch all\" as one logical operation. Each page is its own API call and competes for tokens with everything else.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Two practical rules:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Don't hold the worker idle while paginating.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"processing","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"of page N as a separate job, and queue a \"fetch page N+1\" job with a delay equal to the wait time. Each page becomes its own atomic unit.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Always batch where HubSpot offers it.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/{type}/batch/{read|update|create}","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"accepts up to 100 IDs per call. For your \"patching IDs\" flow this is the difference between 600 calls and 6.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Walkthrough: 600 opportunities","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Walkthrough: 600 opportunities","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Sane flow:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Identify deltas","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— if you can avoid Search at all, do. Use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Batch read","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"current state —","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/deals/batch/read","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with 100 IDs per call → 6 calls. Burst weight 6.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Compute diff","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"in your code (no API).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Batch update","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"—","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/deals/batch/update","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with 100 per call → 6 calls.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Associations","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"—","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v4/associations/{from}/{to}/batch/create","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"if needed → another few batch calls.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Recognition: pre-check denies if","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"<weight>","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"slots aren't free; on 429 the response surfaces","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"policyName","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(DAILY/SECONDLY) — log it, push the job back with","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry-After","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"delay, increment a metric so you can alarm on sustained 429 rate.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Pitfalls & bottlenecks worth pre-empting","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Pitfalls & bottlenecks worth pre-empting","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Daily quota is shared across the whole portal","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search is uniquely fragile","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":": separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hs_object_id","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"ranges or by","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"windows.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry storms after HubSpot incidents","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Webhook → sync → webhook loops","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"jy_last_sync_id","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":") and ignore webhooks where it just changed.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Token refresh","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"expires_in - 60s","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", single-flight the refresh (only one worker refreshes; others wait on a lock).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search latency for fresh writes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Clock skew","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— Redis is authoritative. Don't use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"time()","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"from each worker for window scoring; use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"redis.call('TIME')","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"inside the Lua script.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Dead workers with held tokens","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The 5% error rule","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Phantom contention with Laravel","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"RateLimited","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"middleware","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":": it's per-job-class by default, not per-portal. 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Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"13:40","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Edit","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"Claude responded: They're independent buckets — with one shared bucket on top.","depth":21,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Claude responded: They're independent buckets — with one shared bucket on top.","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Analyzed independent rate limit buckets and their practical interactions","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Analyzed independent rate limit buckets and their practical interactions","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"They're independent buckets — with one shared bucket on top.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"The three buckets and what each call hits","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"The three buckets and what each call hits","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"┌──────────────────┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search request ──→ │ Search: 5/sec │ ──┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"└──────────────────┘ │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"├──→ ┌─────────────────┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"┌──────────────────┐ │ │ Daily: 250k–1M │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"└──────────────────┘ └─────────────────┘","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"What this means practically","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"What this means practically","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting search secondly limit (5/s):","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Only further","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/search","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting burst limit (190/10s):","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting daily limit:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"So can one type of request block the other?","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"So can one type of request block the other?","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search → Burst:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"No.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Burst → Search:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"No.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Saturating burst leaves Search's 5/s untouched.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Either → Daily:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Yes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", but only by contributing to the same daily counter. It's volume that kills you here, not the type.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"In theory you can sustain ~50 search + ~190 other =","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"~240 calls per 10 seconds","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"indefinitely (until daily runs out), because the two buckets bill independently.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Implementation consequence","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Implementation consequence","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"php","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'search'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// for search endpoints only","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'burst'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// for everything else","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'daily'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// every call, both kinds","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('search')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"+","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('daily')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". A batch update calls","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('burst')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"+","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('daily')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":".","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"One subtle pitfall: when you backoff on a 429,","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"inspect","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"policyName","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"in the response body","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to know which bucket actually fired. If you see","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"SECONDLY","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The other subtlety: the burst response gives you","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give positive feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give negative feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"You said: Ok, so what will happen each time I make a request?","depth":20,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"You said: Ok, so what will happen each time I make a request?","depth":21,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"13:44","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Edit","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.","depth":21,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Analyzed per-request overhead concerns and quantified Redis performance impact","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Analyzed per-request overhead concerns and quantified Redis performance impact","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out. But the overhead is much smaller than it sounds, and importantly, it's nowhere near your bottleneck.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"What \"two buckets per request\" actually costs","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"What \"two buckets per request\" actually costs","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Each request makes","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"one round trip to Redis","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— not two — because you fold both bucket checks into a single Lua script that runs atomically server-side:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to 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Two keys touched. Returns either","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"{1, OK, remaining}","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"or","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"{0, reason, retry_ms}","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". No race conditions because Lua is single-threaded inside Redis. No \"check then increment\" gap that other workers can sneak through.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"The math on whether this is heavy","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"The math on whether this is heavy","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"For your 100,000 requests, with 100 PATCHes batched into 1 batch_update call per 100 deals, you're realistically making more like","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1,000–2,000 actual API calls","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(assuming you're using","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/batch/update","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/batch/read","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"properly). But let's pretend you really make all 100k.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"At HubSpot's ceiling of ~240 calls/sec across both buckets, 100k calls take roughly","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"7 minutes","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"of wall time. That means:","depth":25,"on_screen":false,"role_description":"text"}]...
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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
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Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
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hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
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You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
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Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
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┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
Copy to clipboard
php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.
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You said: Ok, so what will happen each time I make a request?
You said: Ok, so what will happen each time I make a request?
Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?
13:44
Retry
Edit
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Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Analyzed per-request overhead concerns and quantified Redis performance impact
Analyzed per-request overhead concerns and quantified Redis performance impact
Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out. But the overhead is much smaller than it sounds, and importantly, it's nowhere near your bottleneck.
What "two buckets per request" actually costs
What "two buckets per request" actually costs
Each request makes
one round trip to Redis
— not two — because you fold both bucket checks into a single Lua script that runs atomically server-side:
Copy to clipboard
lua
-- KEYS[1] = burst bucket key (or search)
-- KEYS[2] = daily bucket key
-- ARGV[1] = now (ms) ARGV[2] = window_ms
-- ARGV[3] = bucket_max ARGV[4] = daily_max
-- ARGV[5] = request_id ARGV[6] = daily_ttl
-- Trim sliding window
redis
.
call
(
'ZREMRANGEBYSCORE'
,
KEYS
[
1
]
,
0
,
ARGV
[
1
]
-
ARGV
[
2
]
)
local
burst_used
=
redis
.
call
(
'ZCARD'
,
KEYS
[
1
]
)
local
daily_used
=
tonumber
(
redis
.
call
(
'GET'
,
KEYS
[
2
]
)
or
'0'
)
if
burst_used
>=
tonumber
(
ARGV
[
3
]
)
then
-- Tell caller how long to sleep until oldest entry expires
local
oldest
=
redis
.
call
(
'ZRANGE'
,
KEYS
[
1
]
,
0
,
0
,
'WITHSCORES'
)
return
{
0
,
'BURST'
,
(
oldest
[
2
]
+
ARGV
[
2
]
)
-
ARGV
[
1
]
}
end
if
daily_used
>=
tonumber
(
ARGV
[
4
]
)
then
return
{
0
,
'DAILY'
,
-
1
}
end
redis
.
call
(
'ZADD'
,
KEYS
[
1
]
,
ARGV
[
1
]
,
ARGV
[
5
]
)
redis
.
call
(
'PEXPIRE'
,
KEYS
[
1
]
,
ARGV
[
2
]
+
1000
)
local
d
=
redis
.
call
(
'INCR'
,
KEYS
[
2
]
)
if
d
==
1
then
redis
.
call
(
'EXPIRE'
,
KEYS
[
2
]
,
ARGV
[
6
]
)
end
return
{
1
,
'OK'
,
tonumber
(
ARGV
[
3
]
)
-
burst_used
-
1
}
One
EVALSHA
call. Two keys touched. Returns either
{1, OK, remaining}
or
{0, reason, retry_ms}
. No race conditions because Lua is single-threaded inside Redis. No "check then increment" gap that other workers can sneak through.
The math on whether this is heavy
The math on whether this is heavy
For your 100,000 requests, with 100 PATCHes batched into 1 batch_update call per 100 deals, you're realistically making more like
1,000–2,000 actual API calls
(assuming you're using
/batch/update
and
/batch/read
properly). But let's pretend you really make all 100k.
At HubSpot's ceiling of ~240 calls/sec across both buckets, 100k calls take roughly
7 minutes
of wall time. That means:...
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|
/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778153275572_m2.jpg...
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Claude
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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
Show more
10:42
Retry
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Copy
Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
Copy
Give positive feedback
Give negative feedback
Retry
You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
Retry
Edit
Copy
Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
Copy to clipboard
php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.
Copy
Give positive feedback
Give negative feedback
Retry
You said: Ok, so what will happen each time I make a request?
You said: Ok, so what will happen each time I make a request?
Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?
13:44
Retry
Edit
Copy
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Analyzed per-request overhead concerns and quantified Redis performance impact
Analyzed per-request overhead concerns and quantified Redis performance impact
Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out. But the overhead is much smaller than it sounds, and importantly, it's nowhere near your bottleneck.
What "two buckets per request" actually costs
What "two buckets per request" actually costs
Each request makes
one round trip to Redis
— not two — because you fold both bucket checks into a single Lua script that runs atomically server-side:
Copy to clipboard
lua
-- KEYS[1] = burst bucket key (or search)
-- KEYS[2] = daily bucket key
-- ARGV[1] = now (ms) ARGV[2] = window_ms
-- ARGV[3] = bucket_max ARGV[4] = daily_max
-- ARGV[5] = request_id ARGV[6] = daily_ttl
-- Trim sliding window
redis
.
call
(
'ZREMRANGEBYSCORE'
,
KEYS
[
1
]
,
0
,
ARGV
[
1
]
-
ARGV
[
2
]
)
local
burst_used
=
redis
.
call
(
'ZCARD'
,
KEYS
[
1
]
)
local
daily_used
=
tonumber
(
redis
.
call
(
'GET'
,
KEYS
[
2
]
)
or
'0'
)
if
burst_used
>=
tonumber
(
ARGV
[
3
]
)
then
-- Tell caller how long to sleep until oldest entry expires
local
oldest...
|
[{"role":"AXLink","text":& [{"role":"AXLink","text":"Skip to content","depth":14,"bounds":{"left":0.029587766,"top":0.03830806,"width":0.0003324468,"height":0.0007980846},"on_screen":true,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Skip to content","depth":15,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Click to 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Think carefully about the implementation and potential issue and bottlenecks.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Show more","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"10:42","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Edit","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"Claude responded: Current HubSpot limits (Apr 2026)","depth":21,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Claude responded: Current HubSpot limits (Apr 2026)","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Current HubSpot limits (Apr 2026)","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Current HubSpot limits (Apr 2026)","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". The burst window is per app; the daily quota is per portal.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". That's the limit the 429 in your example is hitting (","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"secondly limit","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"on","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/crm/v3/objects/contact/search","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"). It's a separate bucket from the 190/10s, and critically, search responses do not include the","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-*","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"headers","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— you can't read remaining capacity off the response, you have to track it yourself.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Non-search endpoints return","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Max","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", and","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Interval-Milliseconds","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". The daily headers are not returned for OAuth-authenticated calls","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— that's enforced for marketplace listing, but it's also a sane operational target.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"So you have","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"four buckets","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Where to store it","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Where to store it","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"INCR","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"EXPIRE","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"plus Lua scripting for true CAS semantics.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Key layout I'd use:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:search:{portalId} ZSET (sliding 1s)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:meta:{portalId} HASH (last seen headers)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The two short-window buckets are sliding-window-log (Redis ZSET, score =","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"microtime","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The daily bucket is a plain","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"INCR","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with TTL set to seconds-until-midnight in","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"HubSpot's account timezone","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to read it","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to read it","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Two-phase: optimistic pre-check, authoritative post-update.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Before a call, run the Lua acquire script. If it returns \"denied,\" sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"After the call, parse","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Max","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Interval-Milliseconds","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and store them in","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:meta:{portalId}","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"For search specifically, since headers don't come back, the local counter","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"is","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"not","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"on 429 (you really did make that request).","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to work with multiple jobs","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to work with multiple jobs","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Concurrency cap","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"via","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Queue::throttle()","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Per-tenant fairness","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Priority lanes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot-priority","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot-bulk","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Backoff on 429","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— respect the","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry-After","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"header. If absent, exponential with jitter (e.g.,","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"min(2^attempt * 250ms, 30s) ± 20%","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to work with paginated requests","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to work with paginated requests","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The trap is treating \"fetch all\" as one logical operation. Each page is its own API call and competes for tokens with everything else.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Two practical rules:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Don't hold the worker idle while paginating.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"processing","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"of page N as a separate job, and queue a \"fetch page N+1\" job with a delay equal to the wait time. Each page becomes its own atomic unit.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Always batch where HubSpot offers it.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/{type}/batch/{read|update|create}","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"accepts up to 100 IDs per call. For your \"patching IDs\" flow this is the difference between 600 calls and 6.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Walkthrough: 600 opportunities","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Walkthrough: 600 opportunities","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Sane flow:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Identify deltas","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— if you can avoid Search at all, do. Use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Batch read","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"current state —","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/deals/batch/read","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with 100 IDs per call → 6 calls. Burst weight 6.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Compute diff","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"in your code (no API).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Batch update","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"—","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/deals/batch/update","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with 100 per call → 6 calls.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Associations","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"—","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v4/associations/{from}/{to}/batch/create","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"if needed → another few batch calls.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Recognition: pre-check denies if","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"<weight>","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"slots aren't free; on 429 the response surfaces","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"policyName","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(DAILY/SECONDLY) — log it, push the job back with","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry-After","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"delay, increment a metric so you can alarm on sustained 429 rate.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Pitfalls & bottlenecks worth pre-empting","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Pitfalls & bottlenecks worth pre-empting","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Daily quota is shared across the whole portal","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search is uniquely fragile","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":": separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hs_object_id","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"ranges or by","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"windows.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry storms after HubSpot incidents","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Webhook → sync → webhook loops","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"jy_last_sync_id","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":") and ignore webhooks where it just changed.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Token refresh","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"expires_in - 60s","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", single-flight the refresh (only one worker refreshes; others wait on a lock).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search latency for fresh writes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Clock skew","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— Redis is authoritative. Don't use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"time()","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"from each worker for window scoring; use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"redis.call('TIME')","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"inside the Lua script.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Dead workers with held tokens","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The 5% error rule","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Phantom contention with Laravel","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"RateLimited","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"middleware","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":": it's per-job-class by default, not per-portal. 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Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"13:40","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Edit","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"Claude responded: They're independent buckets — with one shared bucket on top.","depth":21,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Claude responded: They're independent buckets — with one shared bucket on top.","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Analyzed independent rate limit buckets and their practical interactions","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Analyzed independent rate limit buckets and their practical interactions","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"They're independent buckets — with one shared bucket on top.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"The three buckets and what each call hits","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"The three buckets and what each call hits","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"┌──────────────────┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search request ──→ │ Search: 5/sec │ ──┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"└──────────────────┘ │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"├──→ ┌─────────────────┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"┌──────────────────┐ │ │ Daily: 250k–1M │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"└──────────────────┘ └─────────────────┘","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"What this means practically","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"What this means practically","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting search secondly limit (5/s):","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Only further","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/search","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting burst limit (190/10s):","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting daily limit:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"So can one type of request block the other?","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"So can one type of request block the other?","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search → Burst:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"No.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Burst → Search:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"No.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Saturating burst leaves Search's 5/s untouched.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Either → Daily:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Yes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", but only by contributing to the same daily counter. It's volume that kills you here, not the type.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"In theory you can sustain ~50 search + ~190 other =","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"~240 calls per 10 seconds","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"indefinitely (until daily runs out), because the two buckets bill independently.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Implementation consequence","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Implementation consequence","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"php","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'search'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// for search endpoints only","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'burst'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// for everything else","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'daily'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// every call, both kinds","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('search')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"+","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('daily')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". A batch update calls","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('burst')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"+","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('daily')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":".","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"One subtle pitfall: when you backoff on a 429,","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"inspect","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"policyName","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"in the response body","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to know which bucket actually fired. If you see","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"SECONDLY","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The other subtlety: the burst response gives you","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give positive feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give negative feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"You said: Ok, so what will happen each time I make a request?","depth":20,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"You said: Ok, so what will happen each time I make a request?","depth":21,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"13:44","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Edit","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.","depth":21,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Analyzed per-request overhead concerns and quantified Redis performance impact","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Analyzed per-request overhead concerns and quantified Redis performance impact","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out. But the overhead is much smaller than it sounds, and importantly, it's nowhere near your bottleneck.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"What \"two buckets per request\" actually costs","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"What \"two buckets per request\" actually costs","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Each request makes","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"one round trip to Redis","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— not two — because you fold both bucket checks into a single Lua script that runs atomically server-side:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"lua","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-- KEYS[1] = burst bucket key (or search)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-- KEYS[2] = daily bucket key","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-- ARGV[1] = now (ms) ARGV[2] = window_ms","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-- ARGV[3] = bucket_max ARGV[4] = daily_max","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-- ARGV[5] = request_id ARGV[6] = daily_ttl","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-- Trim sliding window","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"redis","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":".","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"call","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'ZREMRANGEBYSCORE'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"KEYS","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"[","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"]","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"0","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"ARGV","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"[","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"]","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"ARGV","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"[","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"2","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"]","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"local","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"burst_used","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"redis","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":".","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"call","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'ZCARD'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"KEYS","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"[","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"]","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"local","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"daily_used","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"tonumber","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"redis","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":".","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"call","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'GET'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"KEYS","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"[","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"2","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"]","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"or","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'0'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"if","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"burst_used","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":">=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"tonumber","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"ARGV","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"[","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"3","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"]","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"then","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-- Tell caller how long to sleep until oldest entry expires","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"local","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"oldest","depth":27,"on_screen":false,"role_description":"text"}]...
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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
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10:42
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Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
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You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
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Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
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┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
Copy to clipboard
php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.
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You said: Ok, so what will happen each time I make a request?
You said: Ok, so what will happen each time I make a request?
Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?
13:44
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Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Analyzed per-request overhead concerns and quantified Redis performance impact
Analyzed per-request overhead concerns and quantified Redis performance impact
Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out. But the overhead is much smaller than it sounds, and importantly, it's nowhere near your bottleneck.
What "two buckets per request" actually costs
What "two buckets per request" actually costs
Each request makes
one round trip to Redis
— not two — because you fold both bucket checks into a single Lua script that runs atomically server-side:
Copy to clipboard
lua
-- KEYS[1] = burst bucket key (or search)
-- KEYS[2] = daily bucket key
-- ARGV[1] = now (ms) ARGV[2] = window_ms
-- ARGV[3] = bucket_max ARGV[4] = daily_max
-- ARGV[5] = request_id ARGV[6] = daily_ttl
-- Trim sliding window
redis
.
call
(
'ZREMRANGEBYSCORE'
,
KEYS
[
1
]
,
0
,
ARGV
[
1
]
-
ARGV
[
2
]
)
local
burst_used
=
redis
.
call
(
'ZCARD'
,
KEYS
[
1
]
)
local
daily_used
=
tonumber
(
redis
.
call
(
'GET'
,
KEYS
[
2
]
)
or
'0'
)
if
burst_used
>=
tonumber
(
ARGV
[
3
]
)
then
-- Tell caller how long to sleep until oldest entry expires
local
oldest...
|
NULL
|
NULL
|
NULL
|
NULL
|
|
2568
|
108
|
21
|
2026-05-07T11:28:01.639111+00:00
|
/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778153281639_m2.jpg...
|
Firefox
|
Search the CRM - HubSpot docs — Work
|
True
|
developers.hubspot.com/docs/api-reference/latest/c developers.hubspot.com/docs/api-reference/latest/crm/search-the-crm#limits...
|
monitor_2
|
NULL
|
NULL
|
NULL
|
NULL
|
Platform Sprint 3 Q2 - Platform Team - Scrum Board Platform Sprint 3 Q2 - Platform Team - Scrum Board - Jira
Platform Sprint 3 Q2 - Platform Team - Scrum Board - Jira
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST [URL_WITH_CREDENTIALS]
}
]
}
]
}
Each object that you search will include a set of
default properties
default properties
that gets returned. For contacts, a search will return
createdate
,
email
,
firstname
,
hs_object_id
,
lastmodifieddate
, and
lastname
. For example, the above request would return the following response:
Report incorrect code
Copy the contents from the code block
Ask AI
{
"total"
:
2
,
"results"
: [
{
"id"
:
"100451"
,
"properties"
: {
"createdate"
:
"2024-01-17T19:55:04.281Z"
,
"email"
:
"[EMAIL]"
,
"firstname"
:
"Test"
,
"hs_object_id"
:
"100451"
,
"lastmodifieddate"
:
"2024-09-11T13:27:39.356Z"
,
"lastname"
:
"Person"
},
"createdAt"
:
"2024-01-17T19:55:04.281Z"
,
"updatedAt"
:
"2024-09-11T13:27:39.356Z"
,
"archived"
:
false
},
{
"id"
:
"57156923994"
,
"properties"
: {
"createdate"
:
"2024-09-11T18:21:50.012Z"
,
"email"
:
"[EMAIL]"
,
"firstname"
:
"Maria"
,
"hs_object_id"
:
"57156923994"
,
"lastmodifieddate"
:
"2024-10-21T21:36:02.961Z"
,
"lastname"
:
"Johnson (Sample Contact)"
},
"createdAt"
:
"2024-09-11T18:21:50.012Z"
,
"updatedAt"
:
"2024-10-21T21:36:02.961Z"
,
"archived"
:
false
}
]
}
To return a specific set of properties, include a
properties
array in the request body. For example:
Report incorrect code
Copy the contents from the code block
Ask AI
{
"filterGroups"
: [
{
"filters"
: [
{
"propertyName"
:
"annualrevenue"
,
"operator"
:
"GT"
,
"value"
:
"10000000"
}
]
}
],
"properties"
: [
"annualrevenue"
,
"name"
]
}
The response for the above request would look like:
Report incorrect code
Copy the contents from the code block
Ask AI
{
"total"
:
38
,
"results"
: [
{
"id"
:
"2810868468"
,
"properties"
: {
"annualrevenue"
:...
|
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[","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"{","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"id\"","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":":","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"2810868468\"","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":",","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"properties\"","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":": {","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"annualrevenue\"","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":":","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"}]...
|
-7533346305400462535
|
-1235816196114513968
|
visual_change
|
accessibility
|
NULL
|
Platform Sprint 3 Q2 - Platform Team - Scrum Board Platform Sprint 3 Q2 - Platform Team - Scrum Board - Jira
Platform Sprint 3 Q2 - Platform Team - Scrum Board - Jira
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST [URL_WITH_CREDENTIALS]
}
]
}
]
}
Each object that you search will include a set of
default properties
default properties
that gets returned. For contacts, a search will return
createdate
,
email
,
firstname
,
hs_object_id
,
lastmodifieddate
, and
lastname
. For example, the above request would return the following response:
Report incorrect code
Copy the contents from the code block
Ask AI
{
"total"
:
2
,
"results"
: [
{
"id"
:
"100451"
,
"properties"
: {
"createdate"
:
"2024-01-17T19:55:04.281Z"
,
"email"
:
"[EMAIL]"
,
"firstname"
:
"Test"
,
"hs_object_id"
:
"100451"
,
"lastmodifieddate"
:
"2024-09-11T13:27:39.356Z"
,
"lastname"
:
"Person"
},
"createdAt"
:
"2024-01-17T19:55:04.281Z"
,
"updatedAt"
:
"2024-09-11T13:27:39.356Z"
,
"archived"
:
false
},
{
"id"
:
"57156923994"
,
"properties"
: {
"createdate"
:
"2024-09-11T18:21:50.012Z"
,
"email"
:
"[EMAIL]"
,
"firstname"
:
"Maria"
,
"hs_object_id"
:
"57156923994"
,
"lastmodifieddate"
:
"2024-10-21T21:36:02.961Z"
,
"lastname"
:
"Johnson (Sample Contact)"
},
"createdAt"
:
"2024-09-11T18:21:50.012Z"
,
"updatedAt"
:
"2024-10-21T21:36:02.961Z"
,
"archived"
:
false
}
]
}
To return a specific set of properties, include a
properties
array in the request body. For example:
Report incorrect code
Copy the contents from the code block
Ask AI
{
"filterGroups"
: [
{
"filters"
: [
{
"propertyName"
:
"annualrevenue"
,
"operator"
:
"GT"
,
"value"
:
"10000000"
}
]
}
],
"properties"
: [
"annualrevenue"
,
"name"
]
}
The response for the above request would look like:
Report incorrect code
Copy the contents from the code block
Ask AI
{
"total"
:
38
,
"results"
: [
{
"id"
:
"2810868468"
,
"properties"
: {
"annualrevenue"
:...
|
2567
|
NULL
|
NULL
|
NULL
|
|
2569
|
108
|
22
|
2026-05-07T11:28:02.632433+00:00
|
/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778153282632_m2.jpg...
|
Firefox
|
Search the CRM - HubSpot docs — Work
|
True
|
developers.hubspot.com/docs/api-reference/latest/c developers.hubspot.com/docs/api-reference/latest/crm/search-the-crm#limits...
|
monitor_2
|
NULL
|
NULL
|
NULL
|
NULL
|
Platform Sprint 3 Q2 - Platform Team - Scrum Board Platform Sprint 3 Q2 - Platform Team - Scrum Board - Jira
Platform Sprint 3 Q2 - Platform Team - Scrum Board - Jira
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
Service-Desk - Queues - Platform team - Service space - Jira
Service-Desk - Queues - Platform team - Service space - Jira
Jy 20807 check various issues with stages by nikolaybiaivanov · Pull Request #12041 · jiminny/app
Jy 20807 check various issues with stages by nikolaybiaivanov · Pull Request #12041 · jiminny/app
Sentry
Sentry
Pull requests · jiminny/app
Pull requests · jiminny/app
Userpilot | Ask Jiminny Report Generated
Userpilot | Ask Jiminny Report Generated
JY-20773 fix user pilot tracking ofr automated report generated by LakyLak · Pull Request #12024 · jiminny/app
JY-20773 fix user pilot tracking ofr automated report generated by LakyLak · Pull Request #12024 · jiminny/app
Jiminny
Jiminny
Search the CRM - HubSpot docs
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|
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Platform Sprint 3 Q2 - Platform Team - Scrum Board Platform Sprint 3 Q2 - Platform Team - Scrum Board - Jira
Platform Sprint 3 Q2 - Platform Team - Scrum Board - Jira
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
Service-Desk - Queues - Platform team - Service space - Jira
Service-Desk - Queues - Platform team - Service space - Jira
Jy 20807 check various issues with stages by nikolaybiaivanov · Pull Request #12041 · jiminny/app
Jy 20807 check various issues with stages by nikolaybiaivanov · Pull Request #12041 · jiminny/app
Sentry
Sentry
Pull requests · jiminny/app
Pull requests · jiminny/app
Userpilot | Ask Jiminny Report Generated
Userpilot | Ask Jiminny Report Generated
JY-20773 fix user pilot tracking ofr automated report generated by LakyLak · Pull Request #12024 · jiminny/app
JY-20773 fix user pilot tracking ofr automated report generated by LakyLak · Pull Request #12024 · jiminny/app
Jiminny
Jiminny
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PostmanEditVIeWWindowmelpHubSpot rate limit imple PostmanEditVIeWWindowmelpHubSpot rate limit implementation strategylimit; integrations using Auth instead have a per-second Af1 burst limit or 100 calis persccones ver aoo ver vorlal. 1nalsshlce veel lasce lo 11u 1us. HudspotWhat this means in practiceFor a public OAuth app like Jiminny, the only real ceiling on volume per portal is the burssustained at the cap, that's:110 calls / 10 sec= 11/sec-000/mLn1= 39.600/hour= 050.400/davSo you could theoretically push ~950k calls per portal per day without hitting any cap -is the only thing throttling you.What vou still need to trackThis actually simplifies your limiter design. For OAuth apps, you only needuimitActionRurst 110/10cPer-portal Redis sliding window (you already have this)Cearch S/cecPer-portal Redis sliding window (vou already have this)DalMNot applicable. Skip the bucket entirelyYou can drop the hubspot:rl:dailv:portalld} counter from vour design. No dailvnudœetino no reconciliation. no midnioht reser ooic. The laccount-info/v3/ani-usage/daily/private-apps endpoint is irrelevant for vour situation - it tracks private alinstallled in the customer's nortal. which is none of vour businessIwo real constraints that aren t quotas but still matter1. The 5% error rule. Requests resulting in an error response shouldn't exceed 5% of tcdally requests; this is enforced for marketplace listing and certincation. So while theidaily call cap, sustained 429s do count against your marketplace standing. Your pre-‹throttling is what keeps this clean. hubspotKeep going in Claude CodeSwitch to Claude Code and let Claude work directiv in vour renorunning and testing as it goesWrite a messageOous 4.7 AdaptilClaude ic Aland can mako mictakoc Plesco double-chock cited courcadPlatform Sprint 3 Q2SevenShores\HubspeService-Desk - Queu• Jy 20807 check varioa Sentry••Pull requests • jiminnyU Useroilot I Ask JiminJY-20773 fix user pilcaJiminnuSearch the CRM - Hubsuppont Dally • In 32mXx Hubspot vYour team is now on the Free plan with 1 admin. You retain editing access and other members are read-only. View team permissions to see who can edit, or upgrade to restore collaboration.v COLLECtIONs, Contactsv CRM Objectsv crm/vslobiects/obiect Twoe,> U batchv ooiect Id›> • associations/{to Object Type)GET Read20: An error occurred.eg. successful operationDEL Archive> PATCH Update> GET List> POST Filter, Sort, and Search CRM Objects> CRM Owners> CRM Pinelinec> Dealsv Engagements> D OLD ENGAGEMENTSGET list meetingsPOST search moditied companiesPOST search tackeGET read call> POST soarch callsGET list callsPOST meetings scheduledGET get meetinaPoSt aet link to tack> POST Create Contact with Association> HubsnotJournal & webhoooks v4post Get tokenGET det suoscriotionsPOST create subscrintiorGET Journal earliestGET Journal latestGeT httos:nuosoot-yGET next offsetPOST aet Token prodDEL DELETE CLIRCCDIDTION DEP DOPTAIICAMIDONMCNTC) spFcs>FLOWSConnect Git E Console 2 TermirGET next off. •GET ReadGET read ciGET Get EngNo environmentvCRM Obiects > crm/v3/obiects/{obiect Tvoel > (obiect Id) > Read) SaveToaseurl)) /crm/vs/objects/call/4801/1536580/properties=boby.internalmeetingnotescassociations=contacis,deals,companiesE Docs Params • Authorization • Headers 9 Body Scripts SettingsCookiesQuery ParamsKeyv propertiesDescriotionBulk Edit ..propertesboby.internalmeetingNotesname<string>companiesfalsefalse205825333040A comma separated list or the properties to be returned inA comma separated list of the properties to be returned inA comma separated list of obiect tvoes to retrieve associalA comma separated list of obiect types to retrieve associalWhether to return only results that have been archived,The name of a property whose values are unique for this olassociationscontacis. deals companiesDescriptionHeaders 21 lest Results404 Not Found • 162 ms • 1.2 KB • Cêe.g- save kesponse .:statusThu. 07 May 2026 11:25•25 GMTwovt/htmlcharcotcica.0950.1cf-ray9f7fdd2hea988428-SOfct-cache-statusDYNAMICcache-controlmust-revalidate,no-cache,no-storestrict-transnort-securitvmax-ade-31536000- includeSubDomains: preloadorigin, Accept-Encodinaaccocc-control-allow-crodentinldserver-timinghcid:desc="019e022f-77c3-7883-a217-66912667639f" cfr.desc="9f7fdd2c02ef3402-IAD"x-hubspot-correlation-id799 917 H691H904y-hubenot-notfoundl10000wwnhonrradhlmmawx-hubspot-ratelimit-remainingx-hubspot-ratelimit-secondlv-remainingrenort-tof"endnoints".f("url"."httnc+|Ma nel.cloudflare com\renort|/v42c=oq20F0hENDQHit@cRe2VRe"success fraction":0.01."report_to"-"cf-nel""max age":604800}100% L2VAIlVariables in requestC baseUrlo token• All VarlablesInu / May 14-20.04httos:/lapi.hubaoCNeR-JHaMxlZoiNd.Giobals Vault Took -- m=m...
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PostmanEditVIeWWindowmelpHubSpot rate limit imple PostmanEditVIeWWindowmelpHubSpot rate limit implementation strategylimit; integrations using Auth instead have a per-second Af1 burst limit or 100 calis persccones ver aoo ver vorlal. 1nalsshlce veel lasce lo 11u 1us. HudspotWhat this means in practiceFor a public OAuth app like Jiminny, the only real ceiling on volume per portal is the burssustained at the cap, that's:110 calls / 10 sec= 11/sec-000/mLn1= 39.600/hour= 050.400/davSo you could theoretically push ~950k calls per portal per day without hitting any cap -is the only thing throttling you.What vou still need to trackThis actually simplifies your limiter design. For OAuth apps, you only needuimitActionRurst 110/10cPer-portal Redis sliding window (you already have this)Cearch S/cecPer-portal Redis sliding window (vou already have this)DalMNot applicable. Skip the bucket entirelyYou can drop the hubspot:rl:dailv:portalld} counter from vour design. No dailvnudœetino no reconciliation. no midnioht reser ooic. The laccount-info/v3/ani-usage/daily/private-apps endpoint is irrelevant for vour situation - it tracks private alinstallled in the customer's nortal. which is none of vour businessIwo real constraints that aren t quotas but still matter1. The 5% error rule. Requests resulting in an error response shouldn't exceed 5% of tcdally requests; this is enforced for marketplace listing and certincation. So while theidaily call cap, sustained 429s do count against your marketplace standing. Your pre-‹throttling is what keeps this clean. hubspotKeep going in Claude CodeSwitch to Claude Code and let Claude work directiv in vour renorunning and testing as it goesWrite a messageOous 4.7 AdaptilClaude ic Aland can mako mictakoc Plesco double-chock cited courcadPlatform Sprint 3 Q2SevenShores\HubspeService-Desk - Queu• Jy 20807 check varioa Sentry••Pull requests • jiminnyU Useroilot I Ask JiminJY-20773 fix user pilcaJiminnuSearch the CRM - Hubsuppont Dally • In 32mXx Hubspot vYour team is now on the Free plan with 1 admin. You retain editing access and other members are read-only. View team permissions to see who can edit, or upgrade to restore collaboration.v COLLECtIONs, Contactsv CRM Objectsv crm/vslobiects/obiect Twoe,> U batchv ooiect Id›> • associations/{to Object Type)GET Read20: An error occurred.eg. successful operationDEL Archive> PATCH Update> GET List> POST Filter, Sort, and Search CRM Objects> CRM Owners> CRM Pinelinec> Dealsv Engagements> D OLD ENGAGEMENTSGET list meetingsPOST search moditied companiesPOST search tackeGET read call> POST soarch callsGET list callsPOST meetings scheduledGET get meetinaPoSt aet link to tack> POST Create Contact with Association> HubsnotJournal & webhoooks v4post Get tokenGET det suoscriotionsPOST create subscrintiorGET Journal earliestGET Journal latestGeT httos:nuosoot-yGET next offsetPOST aet Token prodDEL DELETE CLIRCCDIDTION DEP DOPTAIICAMIDONMCNTC) spFcs>FLOWSConnect Git E Console 2 TermirGET next off. •GET ReadGET read ciGET Get EngNo environmentvCRM Obiects > crm/v3/obiects/{obiect Tvoel > (obiect Id) > Read) SaveToaseurl)) /crm/vs/objects/call/4801/1536580/properties=boby.internalmeetingnotescassociations=contacis,deals,companiesE Docs Params • Authorization • Headers 9 Body Scripts SettingsCookiesQuery ParamsKeyv propertiesDescriotionBulk Edit ..propertesboby.internalmeetingNotesname<string>companiesfalsefalse205825333040A comma separated list or the properties to be returned inA comma separated list of the properties to be returned inA comma separated list of obiect tvoes to retrieve associalA comma separated list of obiect types to retrieve associalWhether to return only results that have been archived,The name of a property whose values are unique for this olassociationscontacis. deals companiesDescriptionHeaders 21 lest Results404 Not Found • 162 ms • 1.2 KB • Cêe.g- save kesponse .:statusThu. 07 May 2026 11:25•25 GMTwovt/htmlcharcotcica.0950.1cf-ray9f7fdd2hea988428-SOfct-cache-statusDYNAMICcache-controlmust-revalidate,no-cache,no-storestrict-transnort-securitvmax-ade-31536000- includeSubDomains: preloadorigin, Accept-Encodinaaccocc-control-allow-crodentinldserver-timinghcid:desc="019e022f-77c3-7883-a217-66912667639f" cfr.desc="9f7fdd2c02ef3402-IAD"x-hubspot-correlation-id799 917 H691H904y-hubenot-notfoundl10000wwnhonrradhlmmawx-hubspot-ratelimit-remainingx-hubspot-ratelimit-secondlv-remainingrenort-tof"endnoints".f("url"."httnc+|Ma nel.cloudflare com\renort|/v42c=oq20F0hENDQHit@cRe2VRe"success fraction":0.01."report_to"-"cf-nel""max age":604800}100% L2VAIlVariables in requestC baseUrlo token• All VarlablesInu / May 14-20.04httos:/lapi.hubaoCNeR-JHaMxlZoiNd.Giobals Vault Took -- m=m...
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PostmanEditVIewWindowmelpHubSpot rate limit imple PostmanEditVIewWindowmelpHubSpot rate limit implementation strategylimit; integrations using Auth instead have a per-second Af1 burst limit or 100 calis persccones ver aoo ver vorlal. 1nalsshlce veel lasce lo 11u 1us. HudspotWhat this means in practiceFor a public OAuth app like Jiminny, the only real ceiling on volume per portal is the burssustained at the cap, that's:110 calls / 10 sec= 11/sec-000/mLn1= 39.600/hour= 050.400/davSo you could theoretically push ~950k calls per portal per day without hitting any cap -is the only thing throttling you.What vou still need to trackThis actually simplifies your limiter design. For OAuth apps, you only needuimitActionRurst 110/10cPer-portal Redis sliding window (you already have this)Cearch S/cecPer-portal Redis sliding window (vou already have this)DalMNot applicable. Skip the bucket entirelyYou can drop the hubspot:rl:dailv:portalld} counter from vour design. No dailvnudœetino no reconciliation. no midnioht reser ooic. The laccount-info/v3/ani-usage/daily/private-apps endpoint is irrelevant for vour situation - it tracks private alinstallled in the customer's nortal. which is none of vour businessIwo real constraints that aren t quotas but still matter1. The 5% error rule. Requests resulting in an error response shouldn't exceed 5% of tcdally requests; this is enforced for marketplace listing and certincation. So while theidaily call cap, sustained 429s do count against your marketplace standing. Your pre-‹throttling is what keeps this clean. hubspotKeep going in Claude CodeSwitch to Claude Code and let Claude work directiv in vour renorunning and testing as it goesWrite a messageOous 4.7 AdaptilClaude ic Aland can mako mictakoc Plesco double-chock cited courcadPlatform Sprint 3 Q2SevenShores\HubspeService-Desk - Queu• Jy 20807 check varioa Sentry••Pull requests • jiminnyU Useroilot I Ask JiminJY-20773 fix user pilc8JiminnySearch the CRM - Hubsuppont Dally • In 32mXx Hubspot vYour team is now on the Free plan with 1 admin. You retain editing access and other members are read-only. View team permissions to see who can edit, or upgrade to restore collaboration.v COLLECtIONs, Contactsv CRM Objectsv crm/vslobiects/obiect Twoe,> U batchv ooiect Id›> • associations/{to Object Type)GET Read20: An error occurred.eg. successful operationDEL Archive> PATCH Update> GET List> POST Filter, Sort, and Search CRM Objects> CRM Owners> CRM Pinelinec> Dealsv Engagements> D OLD ENGAGEMENTSGET list meetingsPOST search moditied companiesPOST search tackeGET read call> POST soarch callsGET list callsPOST meetings scheduledGET get meetinaPoSt aet link to tack> POST Create Contact with Association> HubsnotJournal & webhoooks v4post Get tokenGET det suoscriotionsPOST create subscrintiorGET Journal earliestGET Journal latestGeT httos:nuosoot-yGET next offsetPOST aet Token prodDEL DELETE CLIRCCDIDTION DEP DOPTAIICAMIDONMCNTC) spFcs>FLOWSConnect Git E Console 2 TermirGET next off. •GET read ciGET Get EngNo environmentvCRM Obiects > crm/v3/obiects/{obiect Tvoel > (obiect Id) > Read) SaveToaseurl)) /crm/vs/objects/call/4801/1536580/properties=boby.internalmeetingnotescassociations=contacis,deals,companiesE Docs Params • Authorization • Headers 9 Body Scripts SettingsCookiesQuery ParamsKeyv propertiesDescriotionBulk Edit ..propertesboby.internalmeetingNotesname<string>companiesfalsefalse205825333040A comma separated list or the properties to be returned inA comma separated list of the properties to be returned inA comma separated list of obiect tvoes to retrieve associalA comma separated list of obiect types to retrieve associalWhether to return only results that have been archived,The name of a property whose values are unique for this olassociationscontacis. deals companiesDescriptionHeaders 21 lest Results404 Not Found • 162 ms • 1.2 KB • Cêe.g- save kesponse .:statusThu. 07 May 2026 11:25•25 GMTwovt/htmlcharcotcica.0950.1cf-ray9f7fdd2hea988428-SOfct-cache-statusDYNAMICcache-controlmust-revalidate,no-cache,no-storestrict-transnort-securitvmax-ade-31536000- includeSubDomains: preloadorigin, Accept-Encodinaaccocc-control-allow-crodentinldserver-timingx-hubspot-correlation-idhcid:desc="019e022f-77c3-7883-a217-66912667639f" cfr.desc="9f7fdd2c02ef3402-IAD"799 917 H691H904y-hubenot-notfoundl10000wwnhonrradhlmmawx-hubspot-ratelimit-remainingx-hubspot-ratelimit-secondlv-remainingrenort-tof"endnoints".f("url"."httnc+|Ma nel.cloudflare com\renort|/v42c=oq20F0hENDQHit@cRe2VRe"success fraction":0.01."report_to"-"cf-nel""max age":604800}100% L2VAIlVariables in requestc baseUrlo token• All VarlablesInu / May 14-20.30httos:/lapi.hubaoCNeR-JHaMxlZoiNd.Giobals Vault Took -- m=m...
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-5288107575736381738
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idle
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PostmanEditVIewWindowmelpHubSpot rate limit imple PostmanEditVIewWindowmelpHubSpot rate limit implementation strategylimit; integrations using Auth instead have a per-second Af1 burst limit or 100 calis persccones ver aoo ver vorlal. 1nalsshlce veel lasce lo 11u 1us. HudspotWhat this means in practiceFor a public OAuth app like Jiminny, the only real ceiling on volume per portal is the burssustained at the cap, that's:110 calls / 10 sec= 11/sec-000/mLn1= 39.600/hour= 050.400/davSo you could theoretically push ~950k calls per portal per day without hitting any cap -is the only thing throttling you.What vou still need to trackThis actually simplifies your limiter design. For OAuth apps, you only needuimitActionRurst 110/10cPer-portal Redis sliding window (you already have this)Cearch S/cecPer-portal Redis sliding window (vou already have this)DalMNot applicable. Skip the bucket entirelyYou can drop the hubspot:rl:dailv:portalld} counter from vour design. No dailvnudœetino no reconciliation. no midnioht reser ooic. The laccount-info/v3/ani-usage/daily/private-apps endpoint is irrelevant for vour situation - it tracks private alinstallled in the customer's nortal. which is none of vour businessIwo real constraints that aren t quotas but still matter1. The 5% error rule. Requests resulting in an error response shouldn't exceed 5% of tcdally requests; this is enforced for marketplace listing and certincation. So while theidaily call cap, sustained 429s do count against your marketplace standing. Your pre-‹throttling is what keeps this clean. hubspotKeep going in Claude CodeSwitch to Claude Code and let Claude work directiv in vour renorunning and testing as it goesWrite a messageOous 4.7 AdaptilClaude ic Aland can mako mictakoc Plesco double-chock cited courcadPlatform Sprint 3 Q2SevenShores\HubspeService-Desk - Queu• Jy 20807 check varioa Sentry••Pull requests • jiminnyU Useroilot I Ask JiminJY-20773 fix user pilc8JiminnySearch the CRM - Hubsuppont Dally • In 32mXx Hubspot vYour team is now on the Free plan with 1 admin. You retain editing access and other members are read-only. View team permissions to see who can edit, or upgrade to restore collaboration.v COLLECtIONs, Contactsv CRM Objectsv crm/vslobiects/obiect Twoe,> U batchv ooiect Id›> • associations/{to Object Type)GET Read20: An error occurred.eg. successful operationDEL Archive> PATCH Update> GET List> POST Filter, Sort, and Search CRM Objects> CRM Owners> CRM Pinelinec> Dealsv Engagements> D OLD ENGAGEMENTSGET list meetingsPOST search moditied companiesPOST search tackeGET read call> POST soarch callsGET list callsPOST meetings scheduledGET get meetinaPoSt aet link to tack> POST Create Contact with Association> HubsnotJournal & webhoooks v4post Get tokenGET det suoscriotionsPOST create subscrintiorGET Journal earliestGET Journal latestGeT httos:nuosoot-yGET next offsetPOST aet Token prodDEL DELETE CLIRCCDIDTION DEP DOPTAIICAMIDONMCNTC) spFcs>FLOWSConnect Git E Console 2 TermirGET next off. •GET read ciGET Get EngNo environmentvCRM Obiects > crm/v3/obiects/{obiect Tvoel > (obiect Id) > Read) SaveToaseurl)) /crm/vs/objects/call/4801/1536580/properties=boby.internalmeetingnotescassociations=contacis,deals,companiesE Docs Params • Authorization • Headers 9 Body Scripts SettingsCookiesQuery ParamsKeyv propertiesDescriotionBulk Edit ..propertesboby.internalmeetingNotesname<string>companiesfalsefalse205825333040A comma separated list or the properties to be returned inA comma separated list of the properties to be returned inA comma separated list of obiect tvoes to retrieve associalA comma separated list of obiect types to retrieve associalWhether to return only results that have been archived,The name of a property whose values are unique for this olassociationscontacis. deals companiesDescriptionHeaders 21 lest Results404 Not Found • 162 ms • 1.2 KB • Cêe.g- save kesponse .:statusThu. 07 May 2026 11:25•25 GMTwovt/htmlcharcotcica.0950.1cf-ray9f7fdd2hea988428-SOfct-cache-statusDYNAMICcache-controlmust-revalidate,no-cache,no-storestrict-transnort-securitvmax-ade-31536000- includeSubDomains: preloadorigin, Accept-Encodinaaccocc-control-allow-crodentinldserver-timingx-hubspot-correlation-idhcid:desc="019e022f-77c3-7883-a217-66912667639f" cfr.desc="9f7fdd2c02ef3402-IAD"799 917 H691H904y-hubenot-notfoundl10000wwnhonrradhlmmawx-hubspot-ratelimit-remainingx-hubspot-ratelimit-secondlv-remainingrenort-tof"endnoints".f("url"."httnc+|Ma nel.cloudflare com\renort|/v42c=oq20F0hENDQHit@cRe2VRe"success fraction":0.01."report_to"-"cf-nel""max age":604800}100% L2VAIlVariables in requestc baseUrlo token• All VarlablesInu / May 14-20.30httos:/lapi.hubaoCNeR-JHaMxlZoiNd.Giobals Vault Took -- m=m...
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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
Show more
10:42
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Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
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Retry
You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
Retry
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Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
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┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
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php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.
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You said: Ok, so what will happen each time I make a request?
You said: Ok, so what will happen each time I make a request?
Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?
13:44
Retry
Edit
Copy
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Analyzed per-request overhead concerns and quantified Redis performance impact
Analyzed per-request overhead concerns and quantified Redis performance impact
Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out. But the overhead is much smaller than it sounds, and importantly, it's nowhere near your bottleneck.
What "two buckets per request" actually costs
What "two buckets per request" actually costs
Each request makes
one round trip to Redis...
|
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Pro","depth":15,"bounds":{"left":0.0043218085,"top":0.6943336,"width":0.037898935,"height":0.01915403},"on_screen":true,"role_description":"pop-up button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Get apps and extensions","depth":15,"bounds":{"left":0.08277926,"top":0.6943336,"width":0.007978723,"height":0.01915403},"on_screen":true,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"HubSpot rate limit implementation strategy, rename chat","depth":19,"bounds":{"left":0.043218084,"top":0.02793296,"width":0.09773936,"height":0.022346368},"on_screen":true,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"HubSpot rate limit implementation 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The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"updating activity (engagement, meeting, call, note)","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"searching for a company or contact","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"updating deal","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"patching IDs\nReview what the limit is at the moment in the HubSpot documentation and then think of the solution:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"where to store it","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"how to read it","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"how to work with multiple jobs","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines","depth":25,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"). Example of such a error Client error:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST https://api.hubapi.com/crm/v3/objects/contact/search","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"resulted in a","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"429 Too Many Requests","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"response: {\"status\":\"error\",\"message\":\"You have reached your secondly limit.\",\"errorType\":\"RATE_LIMIT\",\"correlationId\":\"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Show more","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"10:42","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Edit","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"Claude responded: Current HubSpot limits (Apr 2026)","depth":21,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Claude responded: Current HubSpot limits (Apr 2026)","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Current HubSpot limits (Apr 2026)","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Current HubSpot limits (Apr 2026)","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". The burst window is per app; the daily quota is per portal.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". That's the limit the 429 in your example is hitting (","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"secondly limit","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"on","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/crm/v3/objects/contact/search","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"). It's a separate bucket from the 190/10s, and critically, search responses do not include the","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-*","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"headers","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— you can't read remaining capacity off the response, you have to track it yourself.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Non-search endpoints return","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Max","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", and","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Interval-Milliseconds","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". The daily headers are not returned for OAuth-authenticated calls","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— that's enforced for marketplace listing, but it's also a sane operational target.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"So you have","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"four buckets","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Where to store it","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Where to store it","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"INCR","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"EXPIRE","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"plus Lua scripting for true CAS semantics.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Key layout I'd use:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:search:{portalId} ZSET (sliding 1s)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:meta:{portalId} HASH (last seen headers)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The two short-window buckets are sliding-window-log (Redis ZSET, score =","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"microtime","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", member = unique request id). 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Get this once and cache it.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to read it","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to read it","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Two-phase: optimistic pre-check, authoritative post-update.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Before a call, run the Lua acquire script. If it returns \"denied,\" sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"After the call, parse","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Max","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Interval-Milliseconds","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and store them in","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:meta:{portalId}","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"For search specifically, since headers don't come back, the local counter","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"is","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"not","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"on 429 (you really did make that request).","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to work with multiple jobs","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to work with multiple jobs","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Concurrency cap","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"via","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Queue::throttle()","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Per-tenant fairness","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Priority lanes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot-priority","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot-bulk","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Backoff on 429","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— respect the","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry-After","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"header. 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Each page is its own API call and competes for tokens with everything else.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Two practical rules:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Don't hold the worker idle while paginating.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"processing","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"of page N as a separate job, and queue a \"fetch page N+1\" job with a delay equal to the wait time. Each page becomes its own atomic unit.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Always batch where HubSpot offers it.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/{type}/batch/{read|update|create}","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"accepts up to 100 IDs per call. For your \"patching IDs\" flow this is the difference between 600 calls and 6.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Walkthrough: 600 opportunities","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Walkthrough: 600 opportunities","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Sane flow:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Identify deltas","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— if you can avoid Search at all, do. Use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Batch read","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"current state —","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/deals/batch/read","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with 100 IDs per call → 6 calls. Burst weight 6.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Compute diff","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"in your code (no API).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Batch update","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"—","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/deals/batch/update","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with 100 per call → 6 calls.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Associations","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"—","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v4/associations/{from}/{to}/batch/create","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"if needed → another few batch calls.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Recognition: pre-check denies if","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"<weight>","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"slots aren't free; on 429 the response surfaces","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"policyName","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(DAILY/SECONDLY) — log it, push the job back with","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry-After","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"delay, increment a metric so you can alarm on sustained 429 rate.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Pitfalls & bottlenecks worth pre-empting","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Pitfalls & bottlenecks worth pre-empting","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Daily quota is shared across the whole portal","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search is uniquely fragile","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":": separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hs_object_id","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"ranges or by","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"windows.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry storms after HubSpot incidents","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Webhook → sync → webhook loops","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"jy_last_sync_id","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":") and ignore webhooks where it just changed.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Token refresh","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"expires_in - 60s","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", single-flight the refresh (only one worker refreshes; others wait on a lock).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search latency for fresh writes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Clock skew","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— Redis is authoritative. Don't use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"time()","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"from each worker for window scoring; use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"redis.call('TIME')","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"inside the Lua script.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Dead workers with held tokens","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The 5% error rule","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Phantom contention with Laravel","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"RateLimited","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"middleware","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":": it's per-job-class by default, not per-portal. If your","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"SyncDealJob","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"UpdateActivityJob","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"HubspotRateLimiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"service that everything routes through.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give positive feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give negative feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"You said: What will happen if there are two different types of rate limits?","depth":20,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"You said: What will happen if there are two different types of rate limits?","depth":21,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"What will happen if there are two different types of rate limits?","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search requests that have 5 requests per second","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"13:40","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Edit","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"Claude responded: They're independent buckets — with one shared bucket on top.","depth":21,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Claude responded: They're independent buckets — with one shared bucket on top.","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Analyzed independent rate limit buckets and their practical interactions","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Analyzed independent rate limit buckets and their practical interactions","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"They're independent buckets — with one shared bucket on top.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"The three buckets and what each call hits","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"The three buckets and what each call hits","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"┌──────────────────┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search request ──→ │ Search: 5/sec │ ──┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"└──────────────────┘ │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"├──→ ┌─────────────────┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"┌──────────────────┐ │ │ Daily: 250k–1M │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"└──────────────────┘ └─────────────────┘","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"What this means practically","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"What this means practically","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting search secondly limit (5/s):","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Only further","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/search","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting burst limit (190/10s):","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting daily limit:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"So can one type of request block the other?","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"So can one type of request block the other?","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search → Burst:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"No.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Burst → Search:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"No.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Saturating burst leaves Search's 5/s untouched.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Either → Daily:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Yes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", but only by contributing to the same daily counter. It's volume that kills you here, not the type.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"In theory you can sustain ~50 search + ~190 other =","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"~240 calls per 10 seconds","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"indefinitely (until daily runs out), because the two buckets bill independently.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Implementation consequence","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Implementation consequence","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"php","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'search'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// for search endpoints only","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'burst'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// for everything else","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'daily'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// every call, both kinds","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('search')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"+","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('daily')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". A batch update calls","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('burst')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"+","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('daily')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":".","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"One subtle pitfall: when you backoff on a 429,","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"inspect","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"policyName","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"in the response body","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to know which bucket actually fired. If you see","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"SECONDLY","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The other subtlety: the burst response gives you","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give positive feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give negative feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"You said: Ok, so what will happen each time I make a request?","depth":20,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"You said: Ok, so what will happen each time I make a request?","depth":21,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"13:44","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Edit","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.","depth":21,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Analyzed per-request overhead concerns and quantified Redis performance impact","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Analyzed per-request overhead concerns and quantified Redis performance impact","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out. But the overhead is much smaller than it sounds, and importantly, it's nowhere near your bottleneck.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"What \"two buckets per request\" actually costs","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"What \"two buckets per request\" actually costs","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Each request makes","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"one round trip to Redis","depth":26,"on_screen":false,"role_description":"text"}]...
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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
Show more
10:42
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Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
Copy
Give positive feedback
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Retry
You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
Retry
Edit
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Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
Copy to clipboard
php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.
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You said: Ok, so what will happen each time I make a request?
You said: Ok, so what will happen each time I make a request?
Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?
13:44
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Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Analyzed per-request overhead concerns and quantified Redis performance impact
Analyzed per-request overhead concerns and quantified Redis performance impact
Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out. But the overhead is much smaller than it sounds, and importantly, it's nowhere near your bottleneck.
What "two buckets per request" actually costs
What "two buckets per request" actually costs
Each request makes
one round trip to Redis...
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/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778153337747_m2.jpg...
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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
Show more
10:42
Retry
Edit
Copy
Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
Copy
Give positive feedback
Give negative feedback
Retry
You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
Retry
Edit
Copy
Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
Copy to clipboard
php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.
Copy
Give positive feedback
Give negative feedback
Retry
You said: Ok, so what will happen each time I make a request?
You said: Ok, so what will happen each time I make a request?
Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?
13:44
Retry
Edit
Copy
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Analyzed per-request overhead concerns and quantified Redis performance impact
Analyzed per-request overhead concerns and quantified Redis performance impact
Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out. But the overhead is much smaller than it sounds, and importantly, it's nowhere near your bottleneck.
What "two buckets per request" actually costs
What "two buckets per request" actually costs
Each request makes
one round trip to Redis
— not two — because you fold both bucket checks into a single Lua script that runs atomically server-side:
Copy to clipboard
lua
-- KEYS[1] = burst bucket key (or search)
-- KEYS[2] = daily bucket key
-- ARGV[1] = now (ms) ARGV[2] = window_ms
-- ARGV[3] = bucket_max ARGV[4] = daily_max
-- ARGV[5] = request_id ARGV[6] = daily_ttl
-- Trim sliding window
redis
.
call
(
'ZREMRANGEBYSCORE'
,
KEYS
[
1
]
,
0
,
ARGV
[
1
]
-
ARGV
[
2...
|
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The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"updating activity (engagement, meeting, call, note)","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"searching for a company or contact","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"updating deal","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"patching IDs\nReview what the limit is at the moment in the HubSpot documentation and then think of the solution:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"where to store it","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"how to read it","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"how to work with multiple jobs","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines","depth":25,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"). Example of such a error Client error:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST https://api.hubapi.com/crm/v3/objects/contact/search","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"resulted in a","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"429 Too Many Requests","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"response: {\"status\":\"error\",\"message\":\"You have reached your secondly limit.\",\"errorType\":\"RATE_LIMIT\",\"correlationId\":\"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Show more","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"10:42","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Edit","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"Claude responded: Current HubSpot limits (Apr 2026)","depth":21,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Claude responded: Current HubSpot limits (Apr 2026)","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Current HubSpot limits (Apr 2026)","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Current HubSpot limits (Apr 2026)","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". The burst window is per app; the daily quota is per portal.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". That's the limit the 429 in your example is hitting (","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"secondly limit","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"on","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/crm/v3/objects/contact/search","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"). 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This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"For search specifically, since headers don't come back, the local counter","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"is","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"the source of truth — meaning if you ever crash mid-flight, you've under-counted. 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Either separate queues per portal or a fair-share scheduler.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Priority lanes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— webhook-driven updates (user-visible latency) should outrank background batch syncs. 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Each page is its own API call and competes for tokens with everything else.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Two practical rules:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Don't hold the worker idle while paginating.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"processing","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"of page N as a separate job, and queue a \"fetch page N+1\" job with a delay equal to the wait time. Each page becomes its own atomic unit.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Always batch where HubSpot offers it.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/{type}/batch/{read|update|create}","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"accepts up to 100 IDs per call. For your \"patching IDs\" flow this is the difference between 600 calls and 6.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Walkthrough: 600 opportunities","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Walkthrough: 600 opportunities","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Sane flow:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Identify deltas","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— if you can avoid Search at all, do. Use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Batch read","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"current state —","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/deals/batch/read","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with 100 IDs per call → 6 calls. Burst weight 6.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Compute diff","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"in your code (no API).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Batch update","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"—","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/deals/batch/update","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with 100 per call → 6 calls.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Associations","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"—","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v4/associations/{from}/{to}/batch/create","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"if needed → another few batch calls.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Recognition: pre-check denies if","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"<weight>","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"slots aren't free; on 429 the response surfaces","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"policyName","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(DAILY/SECONDLY) — log it, push the job back with","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry-After","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"delay, increment a metric so you can alarm on sustained 429 rate.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Pitfalls & bottlenecks worth pre-empting","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Pitfalls & bottlenecks worth pre-empting","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Daily quota is shared across the whole portal","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search is uniquely fragile","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":": separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hs_object_id","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"ranges or by","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"windows.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry storms after HubSpot incidents","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Webhook → sync → webhook loops","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"jy_last_sync_id","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":") and ignore webhooks where it just changed.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Token refresh","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"expires_in - 60s","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", single-flight the refresh (only one worker refreshes; others wait on a lock).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search latency for fresh writes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Clock skew","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— Redis is authoritative. Don't use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"time()","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"from each worker for window scoring; use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"redis.call('TIME')","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"inside the Lua script.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Dead workers with held tokens","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The 5% error rule","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Phantom contention with Laravel","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"RateLimited","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"middleware","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":": it's per-job-class by default, not per-portal. If your","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"SyncDealJob","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"UpdateActivityJob","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"HubspotRateLimiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"service that everything routes through.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give positive feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give negative feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"You said: What will happen if there are two different types of rate limits?","depth":20,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"You said: What will happen if there are two different types of rate limits?","depth":21,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"What will happen if there are two different types of rate limits?","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search requests that have 5 requests per second","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"13:40","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Edit","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"Claude responded: They're independent buckets — with one shared bucket on top.","depth":21,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Claude responded: They're independent buckets — with one shared bucket on top.","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Analyzed independent rate limit buckets and their practical interactions","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Analyzed independent rate limit buckets and their practical interactions","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"They're independent buckets — with one shared bucket on top.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"The three buckets and what each call hits","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"The three buckets and what each call hits","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"┌──────────────────┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search request ──→ │ Search: 5/sec │ ──┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"└──────────────────┘ │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"├──→ ┌─────────────────┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"┌──────────────────┐ │ │ Daily: 250k–1M │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"└──────────────────┘ └─────────────────┘","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"What this means practically","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"What this means practically","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting search secondly limit (5/s):","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Only further","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/search","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting burst limit (190/10s):","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting daily limit:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"So can one type of request block the other?","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"So can one type of request block the other?","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search → Burst:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"No.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Burst → Search:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"No.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Saturating burst leaves Search's 5/s untouched.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Either → Daily:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Yes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", but only by contributing to the same daily counter. It's volume that kills you here, not the type.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"In theory you can sustain ~50 search + ~190 other =","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"~240 calls per 10 seconds","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"indefinitely (until daily runs out), because the two buckets bill independently.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Implementation consequence","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Implementation consequence","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"php","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'search'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// for search endpoints only","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'burst'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// for everything else","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'daily'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// every call, both kinds","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('search')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"+","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('daily')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". A batch update calls","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('burst')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"+","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('daily')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":".","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"One subtle pitfall: when you backoff on a 429,","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"inspect","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"policyName","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"in the response body","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to know which bucket actually fired. If you see","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"SECONDLY","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The other subtlety: the burst response gives you","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give positive feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give negative feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"You said: Ok, so what will happen each time I make a request?","depth":20,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"You said: Ok, so what will happen each time I make a request?","depth":21,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"13:44","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Edit","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.","depth":21,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Analyzed per-request overhead concerns and quantified Redis performance impact","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Analyzed per-request overhead concerns and quantified Redis performance impact","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out. But the overhead is much smaller than it sounds, and importantly, it's nowhere near your bottleneck.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"What \"two buckets per request\" actually costs","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"What \"two buckets per request\" actually costs","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Each request makes","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"one round trip to Redis","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— not two — because you fold both bucket checks into a single Lua script that runs atomically server-side:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"lua","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-- KEYS[1] = burst bucket key (or search)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-- KEYS[2] = daily bucket key","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-- ARGV[1] = now (ms) ARGV[2] = window_ms","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-- ARGV[3] = bucket_max ARGV[4] = daily_max","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-- ARGV[5] = request_id ARGV[6] = daily_ttl","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-- Trim sliding window","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"redis","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":".","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"call","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'ZREMRANGEBYSCORE'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"KEYS","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"[","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"]","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"0","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"ARGV","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"[","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"]","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"ARGV","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"[","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"2","depth":27,"on_screen":false,"role_description":"text"}]...
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7146116803105359797
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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
Show more
10:42
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Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
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You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
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Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
Copy to clipboard
php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.
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You said: Ok, so what will happen each time I make a request?
You said: Ok, so what will happen each time I make a request?
Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?
13:44
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Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Analyzed per-request overhead concerns and quantified Redis performance impact
Analyzed per-request overhead concerns and quantified Redis performance impact
Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out. But the overhead is much smaller than it sounds, and importantly, it's nowhere near your bottleneck.
What "two buckets per request" actually costs
What "two buckets per request" actually costs
Each request makes
one round trip to Redis
— not two — because you fold both bucket checks into a single Lua script that runs atomically server-side:
Copy to clipboard
lua
-- KEYS[1] = burst bucket key (or search)
-- KEYS[2] = daily bucket key
-- ARGV[1] = now (ms) ARGV[2] = window_ms
-- ARGV[3] = bucket_max ARGV[4] = daily_max
-- ARGV[5] = request_id ARGV[6] = daily_ttl
-- Trim sliding window
redis
.
call
(
'ZREMRANGEBYSCORE'
,
KEYS
[
1
]
,
0
,
ARGV
[
1
]
-
ARGV
[
2...
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Claude
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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
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Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
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hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
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You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
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Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
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┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
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php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.
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You said: Ok, so what will happen each time I make a request?
You said: Ok, so what will happen each time I make a request?
Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?
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Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Analyzed per-request overhead concerns and quantified Redis performance impact
Analyzed per-request overhead concerns and quantified Redis performance impact
Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out. But the overhead is much smaller than it sounds, and importantly, it's nowhere near your bottleneck....
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Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The daily bucket is a plain","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"INCR","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with TTL set to seconds-until-midnight in","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"HubSpot's account timezone","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to read it","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to read it","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Two-phase: optimistic pre-check, authoritative post-update.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Before a call, run the Lua acquire script. If it returns \"denied,\" sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"After the call, parse","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Max","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Interval-Milliseconds","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and store them in","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:meta:{portalId}","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"For search specifically, since headers don't come back, the local counter","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"is","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"not","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"on 429 (you really did make that request).","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to work with multiple jobs","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to work with multiple jobs","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Concurrency cap","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"via","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Queue::throttle()","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Per-tenant fairness","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Priority lanes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot-priority","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot-bulk","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Backoff on 429","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— respect the","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry-After","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"header. If absent, exponential with jitter (e.g.,","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"min(2^attempt * 250ms, 30s) ± 20%","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to work with paginated requests","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to work with paginated requests","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The trap is treating \"fetch all\" as one logical operation. Each page is its own API call and competes for tokens with everything else.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Two practical rules:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Don't hold the worker idle while paginating.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"processing","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"of page N as a separate job, and queue a \"fetch page N+1\" job with a delay equal to the wait time. Each page becomes its own atomic unit.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Always batch where HubSpot offers it.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/{type}/batch/{read|update|create}","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"accepts up to 100 IDs per call. For your \"patching IDs\" flow this is the difference between 600 calls and 6.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Walkthrough: 600 opportunities","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Walkthrough: 600 opportunities","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Sane flow:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Identify deltas","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— if you can avoid Search at all, do. Use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Batch read","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"current state —","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/deals/batch/read","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with 100 IDs per call → 6 calls. Burst weight 6.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Compute diff","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"in your code (no API).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Batch update","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"—","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/deals/batch/update","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with 100 per call → 6 calls.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Associations","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"—","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v4/associations/{from}/{to}/batch/create","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"if needed → another few batch calls.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Recognition: pre-check denies if","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"<weight>","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"slots aren't free; on 429 the response surfaces","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"policyName","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(DAILY/SECONDLY) — log it, push the job back with","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry-After","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"delay, increment a metric so you can alarm on sustained 429 rate.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Pitfalls & bottlenecks worth pre-empting","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Pitfalls & bottlenecks worth pre-empting","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Daily quota is shared across the whole portal","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search is uniquely fragile","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":": separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hs_object_id","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"ranges or by","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"windows.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry storms after HubSpot incidents","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Webhook → sync → webhook loops","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"jy_last_sync_id","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":") and ignore webhooks where it just changed.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Token refresh","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"expires_in - 60s","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", single-flight the refresh (only one worker refreshes; others wait on a lock).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search latency for fresh writes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Clock skew","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— Redis is authoritative. Don't use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"time()","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"from each worker for window scoring; use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"redis.call('TIME')","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"inside the Lua script.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Dead workers with held tokens","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The 5% error rule","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Phantom contention with Laravel","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"RateLimited","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"middleware","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":": it's per-job-class by default, not per-portal. If your","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"SyncDealJob","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"UpdateActivityJob","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"HubspotRateLimiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"service that everything routes through.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give positive feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give negative feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"You said: What will happen if there are two different types of rate limits?","depth":20,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"You said: What will happen if there are two different types of rate limits?","depth":21,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"What will happen if there are two different types of rate limits?","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search requests that have 5 requests per second","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"13:40","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Edit","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"Claude responded: They're independent buckets — with one shared bucket on top.","depth":21,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Claude responded: They're independent buckets — with one shared bucket on top.","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Analyzed independent rate limit buckets and their practical interactions","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Analyzed independent rate limit buckets and their practical interactions","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"They're independent buckets — with one shared bucket on top.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"The three buckets and what each call hits","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"The three buckets and what each call hits","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"┌──────────────────┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search request ──→ │ Search: 5/sec │ ──┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"└──────────────────┘ │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"├──→ ┌─────────────────┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"┌──────────────────┐ │ │ Daily: 250k–1M │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"└──────────────────┘ └─────────────────┘","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"What this means practically","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"What this means practically","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting search secondly limit (5/s):","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Only further","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/search","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting burst limit (190/10s):","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting daily limit:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"So can one type of request block the other?","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"So can one type of request block the other?","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search → Burst:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"No.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Burst → Search:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"No.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Saturating burst leaves Search's 5/s untouched.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Either → Daily:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Yes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", but only by contributing to the same daily counter. It's volume that kills you here, not the type.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"In theory you can sustain ~50 search + ~190 other =","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"~240 calls per 10 seconds","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"indefinitely (until daily runs out), because the two buckets bill independently.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Implementation consequence","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Implementation consequence","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"php","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'search'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// for search endpoints only","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'burst'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// for everything else","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'daily'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// every call, both kinds","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('search')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"+","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('daily')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". A batch update calls","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('burst')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"+","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('daily')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":".","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"One subtle pitfall: when you backoff on a 429,","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"inspect","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"policyName","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"in the response body","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to know which bucket actually fired. If you see","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"SECONDLY","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The other subtlety: the burst response gives you","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give positive feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give negative feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"You said: Ok, so what will happen each time I make a request?","depth":20,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"You said: Ok, so what will happen each time I make a request?","depth":21,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"13:44","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Edit","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.","depth":21,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Analyzed per-request overhead concerns and quantified Redis performance impact","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Analyzed per-request overhead concerns and quantified Redis performance impact","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out. But the overhead is much smaller than it sounds, and importantly, it's nowhere near your bottleneck.","depth":25,"on_screen":false,"role_description":"text"}]...
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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
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10:42
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Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
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You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
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Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
Copy to clipboard
php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.
Copy
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Give negative feedback
Retry
You said: Ok, so what will happen each time I make a request?
You said: Ok, so what will happen each time I make a request?
Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?
13:44
Retry
Edit
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Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Analyzed per-request overhead concerns and quantified Redis performance impact
Analyzed per-request overhead concerns and quantified Redis performance impact
Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out. But the overhead is much smaller than it sounds, and importantly, it's nowhere near your bottleneck....
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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
Show more
10:42
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Edit
Copy
Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
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Give positive feedback
Give negative feedback
Retry
You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
Retry
Edit
Copy
Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
Copy to clipboard
php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.
Copy
Give positive feedback
Give negative feedback
Retry
You said: Ok, so what will happen each time I make a request?
You said: Ok, so what will happen each time I make a request?
Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?
13:44
Retry
Edit
Copy
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Analyzed per-request overhead concerns and quantified Redis performance impact
Analyzed per-request overhead concerns and quantified Redis performance impact
Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out. But the overhead is much smaller than it sounds, and importantly, it's nowhere near your bottleneck.
What "two buckets per request" actually costs
What "two buckets per request" actually costs
Each request makes
one round trip to Redis
— not two — because you fold both bucket checks into a single Lua script that runs atomically server-side:
Copy to clipboard
lua
-- KEYS[1] = burst bucket key (or search)
-- KEYS[2] = daily bucket key
-- ARGV[1] = now (ms) ARGV[2] = window_ms
-- ARGV[3] = bucket_max ARGV[4] = daily_max
-- ARGV[5] = request_id ARGV[6] = daily_ttl
-- Trim sliding window
redis
.
call
(
'ZREMRANGEBYSCORE'
,
KEYS
[
1
]
,
0
,
ARGV
[
1
]
-
ARGV
[
2
]
)
local
burst_used
=
redis
.
call
(
'ZCARD'
,
KEYS
[
1
]
)
local
daily_used
=
tonumber
(
redis
.
call
(
'GET'
,
KEYS
[
2
]
)
or
'0'
)
if
burst_used
>=
tonumber
(
ARGV
[
3
]
)
then
-- Tell caller how long to sleep until oldest entry expires
local
oldest
=
redis
.
call
(
'ZRANGE'
,
KEYS
[
1
]
,
0
,
0
,
'WITHSCORES'
)
return
{
0
,
'BURST'
,
(
oldest
[
2
]
+
ARGV
[
2
]
)
-
ARGV
[
1
]
}
end...
|
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That's the limit the 429 in your example is hitting (","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"secondly limit","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"on","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/crm/v3/objects/contact/search","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"). It's a separate bucket from the 190/10s, and critically, search responses do not include the","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-*","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"headers","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— you can't read remaining capacity off the response, you have to track it yourself.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Non-search endpoints return","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Max","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", and","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Interval-Milliseconds","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". The daily headers are not returned for OAuth-authenticated calls","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— that's enforced for marketplace listing, but it's also a sane operational target.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"So you have","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"four buckets","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Where to store it","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Where to store it","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"INCR","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"EXPIRE","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"plus Lua scripting for true CAS semantics.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Key layout I'd use:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:search:{portalId} ZSET (sliding 1s)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:meta:{portalId} HASH (last seen headers)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The two short-window buckets are sliding-window-log (Redis ZSET, score =","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"microtime","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The daily bucket is a plain","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"INCR","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with TTL set to seconds-until-midnight in","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"HubSpot's account timezone","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to read it","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to read it","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Two-phase: optimistic pre-check, authoritative post-update.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Before a call, run the Lua acquire script. If it returns \"denied,\" sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"After the call, parse","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Max","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Interval-Milliseconds","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and store them in","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:meta:{portalId}","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"For search specifically, since headers don't come back, the local counter","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"is","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"not","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"on 429 (you really did make that request).","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to work with multiple jobs","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to work with multiple jobs","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Concurrency cap","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"via","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Queue::throttle()","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Per-tenant fairness","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Priority lanes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot-priority","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot-bulk","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Backoff on 429","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— respect the","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry-After","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"header. If absent, exponential with jitter (e.g.,","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"min(2^attempt * 250ms, 30s) ± 20%","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to work with paginated requests","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to work with paginated requests","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The trap is treating \"fetch all\" as one logical operation. Each page is its own API call and competes for tokens with everything else.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Two practical rules:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Don't hold the worker idle while paginating.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"processing","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"of page N as a separate job, and queue a \"fetch page N+1\" job with a delay equal to the wait time. Each page becomes its own atomic unit.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Always batch where HubSpot offers it.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/{type}/batch/{read|update|create}","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"accepts up to 100 IDs per call. For your \"patching IDs\" flow this is the difference between 600 calls and 6.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Walkthrough: 600 opportunities","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Walkthrough: 600 opportunities","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Sane flow:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Identify deltas","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— if you can avoid Search at all, do. Use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Batch read","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"current state —","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/deals/batch/read","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with 100 IDs per call → 6 calls. Burst weight 6.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Compute diff","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"in your code (no API).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Batch update","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"—","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/deals/batch/update","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with 100 per call → 6 calls.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Associations","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"—","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v4/associations/{from}/{to}/batch/create","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"if needed → another few batch calls.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Recognition: pre-check denies if","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"<weight>","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"slots aren't free; on 429 the response surfaces","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"policyName","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(DAILY/SECONDLY) — log it, push the job back with","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry-After","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"delay, increment a metric so you can alarm on sustained 429 rate.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Pitfalls & bottlenecks worth pre-empting","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Pitfalls & bottlenecks worth pre-empting","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Daily quota is shared across the whole portal","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search is uniquely fragile","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":": separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hs_object_id","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"ranges or by","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"windows.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry storms after HubSpot incidents","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Webhook → sync → webhook loops","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"jy_last_sync_id","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":") and ignore webhooks where it just changed.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Token refresh","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"expires_in - 60s","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", single-flight the refresh (only one worker refreshes; others wait on a lock).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search latency for fresh writes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Clock skew","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— Redis is authoritative. Don't use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"time()","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"from each worker for window scoring; use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"redis.call('TIME')","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"inside the Lua script.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Dead workers with held tokens","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The 5% error rule","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Phantom contention with Laravel","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"RateLimited","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"middleware","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":": it's per-job-class by default, not per-portal. 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Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? 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n":"text"},{"role":"AXStaticText","text":"KEYS","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"[","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"2","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"]","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"or","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'0'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"if","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"burst_used","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":">=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"tonumber","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"ARGV","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"[","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"3","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"]","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"then","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-- Tell caller how long to sleep until oldest entry expires","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"local","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"oldest","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"redis","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":".","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"call","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'ZRANGE'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"KEYS","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"[","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"]","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"0","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"0","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'WITHSCORES'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"return","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"{","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"0","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'BURST'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"oldest","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"[","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"2","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"]","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"+","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"ARGV","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"[","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"2","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"]","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"ARGV","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"[","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"]","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
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Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
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You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
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Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
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┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
Copy to clipboard
php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.
Copy
Give positive feedback
Give negative feedback
Retry
You said: Ok, so what will happen each time I make a request?
You said: Ok, so what will happen each time I make a request?
Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?
13:44
Retry
Edit
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Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Analyzed per-request overhead concerns and quantified Redis performance impact
Analyzed per-request overhead concerns and quantified Redis performance impact
Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out. But the overhead is much smaller than it sounds, and importantly, it's nowhere near your bottleneck.
What "two buckets per request" actually costs
What "two buckets per request" actually costs
Each request makes
one round trip to Redis
— not two — because you fold both bucket checks into a single Lua script that runs atomically server-side:
Copy to clipboard
lua
-- KEYS[1] = burst bucket key (or search)
-- KEYS[2] = daily bucket key
-- ARGV[1] = now (ms) ARGV[2] = window_ms
-- ARGV[3] = bucket_max ARGV[4] = daily_max
-- ARGV[5] = request_id ARGV[6] = daily_ttl
-- Trim sliding window
redis
.
call
(
'ZREMRANGEBYSCORE'
,
KEYS
[
1
]
,
0
,
ARGV
[
1
]
-
ARGV
[
2
]
)
local
burst_used
=
redis
.
call
(
'ZCARD'
,
KEYS
[
1
]
)
local
daily_used
=
tonumber
(
redis
.
call
(
'GET'
,
KEYS
[
2
]
)
or
'0'
)
if
burst_used
>=
tonumber
(
ARGV
[
3
]
)
then
-- Tell caller how long to sleep until oldest entry expires
local
oldest
=
redis
.
call
(
'ZRANGE'
,
KEYS
[
1
]
,
0
,
0
,
'WITHSCORES'
)
return
{
0
,
'BURST'
,
(
oldest
[
2
]
+
ARGV
[
2
]
)
-
ARGV
[
1
]
}
end...
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110
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2
|
2026-05-07T11:29:48.604784+00:00
|
/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778153388604_m2.jpg...
|
Claude
|
Claude
|
True
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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
Show more
10:42
Retry
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Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
Copy
Give positive feedback
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Retry
You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
Retry
Edit
Copy
Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
Copy to clipboard
php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls....
|
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That's the limit the 429 in your example is hitting (","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"secondly limit","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"on","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/crm/v3/objects/contact/search","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"). It's a separate bucket from the 190/10s, and critically, search responses do not include the","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-*","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"headers","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— you can't read remaining capacity off the response, you have to track it yourself.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Non-search endpoints return","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Max","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", and","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Interval-Milliseconds","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". The daily headers are not returned for OAuth-authenticated calls","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— that's enforced for marketplace listing, but it's also a sane operational target.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"So you have","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"four buckets","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Where to store it","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Where to store it","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"INCR","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"EXPIRE","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"plus Lua scripting for true CAS semantics.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Key layout I'd use:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:search:{portalId} ZSET (sliding 1s)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:meta:{portalId} HASH (last seen headers)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The two short-window buckets are sliding-window-log (Redis ZSET, score =","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"microtime","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The daily bucket is a plain","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"INCR","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with TTL set to seconds-until-midnight in","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"HubSpot's account timezone","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to read it","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to read it","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Two-phase: optimistic pre-check, authoritative post-update.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Before a call, run the Lua acquire script. If it returns \"denied,\" sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"After the call, parse","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Max","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Interval-Milliseconds","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and store them in","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:meta:{portalId}","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"For search specifically, since headers don't come back, the local counter","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"is","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"not","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"on 429 (you really did make that request).","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to work with multiple jobs","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to work with multiple jobs","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Concurrency cap","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"via","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Queue::throttle()","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Per-tenant fairness","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Priority lanes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot-priority","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot-bulk","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Backoff on 429","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— respect the","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry-After","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"header. If absent, exponential with jitter (e.g.,","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"min(2^attempt * 250ms, 30s) ± 20%","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to work with paginated requests","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to work with paginated requests","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The trap is treating \"fetch all\" as one logical operation. Each page is its own API call and competes for tokens with everything else.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Two practical rules:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Don't hold the worker idle while paginating.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"processing","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"of page N as a separate job, and queue a \"fetch page N+1\" job with a delay equal to the wait time. Each page becomes its own atomic unit.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Always batch where HubSpot offers it.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/{type}/batch/{read|update|create}","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"accepts up to 100 IDs per call. For your \"patching IDs\" flow this is the difference between 600 calls and 6.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Walkthrough: 600 opportunities","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Walkthrough: 600 opportunities","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Sane flow:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Identify deltas","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— if you can avoid Search at all, do. Use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Batch read","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"current state —","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/deals/batch/read","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with 100 IDs per call → 6 calls. Burst weight 6.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Compute diff","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"in your code (no API).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Batch update","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"—","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/deals/batch/update","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with 100 per call → 6 calls.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Associations","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"—","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v4/associations/{from}/{to}/batch/create","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"if needed → another few batch calls.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Recognition: pre-check denies if","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"<weight>","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"slots aren't free; on 429 the response surfaces","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"policyName","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(DAILY/SECONDLY) — log it, push the job back with","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry-After","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"delay, increment a metric so you can alarm on sustained 429 rate.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Pitfalls & bottlenecks worth pre-empting","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Pitfalls & bottlenecks worth pre-empting","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Daily quota is shared across the whole portal","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search is uniquely fragile","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":": separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hs_object_id","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"ranges or by","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"windows.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry storms after HubSpot incidents","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Webhook → sync → webhook loops","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"jy_last_sync_id","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":") and ignore webhooks where it just changed.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Token refresh","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"expires_in - 60s","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", single-flight the refresh (only one worker refreshes; others wait on a lock).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search latency for fresh writes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Clock skew","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— Redis is authoritative. Don't use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"time()","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"from each worker for window scoring; use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"redis.call('TIME')","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"inside the Lua script.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Dead workers with held tokens","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The 5% error rule","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Phantom contention with Laravel","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"RateLimited","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"middleware","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":": it's per-job-class by default, not per-portal. If your","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"SyncDealJob","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"UpdateActivityJob","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"HubspotRateLimiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"service that everything routes through.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give positive feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give negative feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"You said: What will happen if there are two different types of rate limits?","depth":20,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"You said: What will happen if there are two different types of rate limits?","depth":21,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"What will happen if there are two different types of rate limits?","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search requests that have 5 requests per second","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"13:40","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Edit","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"Claude responded: They're independent buckets — with one shared bucket on top.","depth":21,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Claude responded: They're independent buckets — with one shared bucket on top.","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Analyzed independent rate limit buckets and their practical interactions","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Analyzed independent rate limit buckets and their practical interactions","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"They're independent buckets — with one shared bucket on top.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"The three buckets and what each call hits","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"The three buckets and what each call hits","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"┌──────────────────┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search request ──→ │ Search: 5/sec │ ──┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"└──────────────────┘ │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"├──→ ┌─────────────────┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"┌──────────────────┐ │ │ Daily: 250k–1M │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"└──────────────────┘ └─────────────────┘","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"What this means practically","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"What this means practically","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting search secondly limit (5/s):","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Only further","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/search","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting burst limit (190/10s):","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting daily limit:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"So can one type of request block the other?","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"So can one type of request block the other?","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search → Burst:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"No.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Burst → Search:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"No.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Saturating burst leaves Search's 5/s untouched.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Either → Daily:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Yes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", but only by contributing to the same daily counter. It's volume that kills you here, not the type.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"In theory you can sustain ~50 search + ~190 other =","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"~240 calls per 10 seconds","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"indefinitely (until daily runs out), because the two buckets bill independently.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Implementation consequence","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Implementation consequence","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"php","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'search'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// for search endpoints only","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'burst'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// for everything else","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'daily'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// every call, both kinds","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('search')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"+","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('daily')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". A batch update calls","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('burst')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"+","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('daily')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":".","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"One subtle pitfall: when you backoff on a 429,","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"inspect","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"policyName","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"in the response body","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to know which bucket actually fired. If you see","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"SECONDLY","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The other subtlety: the burst response gives you","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.","depth":25,"on_screen":false,"role_description":"text"}]...
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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
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Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
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You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
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Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
Copy to clipboard
php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls....
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/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778153390538_m2.jpg...
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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
Show more
10:42
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Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
Copy
Give positive feedback
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Retry
You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
Retry
Edit
Copy
Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
Copy to clipboard
php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.
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Retry
You said: Ok, so what will happen each time I make a request?
You said: Ok, so what will happen each time I make a request?
Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?
13:44
Retry
Edit
Copy
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Analyzed per-request overhead concerns and quantified Redis performance impact
Analyzed per-request overhead concerns and quantified Redis performance impact
Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out. But the overhead is much smaller than it sounds, and importantly, it's nowhere near your bottleneck.
What "two buckets per request" actually costs
What "two buckets per request" actually costs
Each request makes
one round trip to Redis
— not two — because you fold both bucket checks into a single Lua script that runs atomically server-side:
Copy to clipboard
lua...
|
[{"role":"AXLink","text":& [{"role":"AXLink","text":"Skip to content","depth":14,"bounds":{"left":0.029587766,"top":0.03830806,"width":0.0003324468,"height":0.0007980846},"on_screen":true,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Skip to content","depth":15,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Click to collapse","depth":16,"bounds":{"left":0.10239362,"top":0.06703911,"width":0.030585106,"height":0.011971269},"on_screen":true,"lines":[{"char_start":0,"char_count":1,"bounds":{"left":0.10239362,"top":0.06703911,"width":0.0029920214,"height":0.011971269}},{"char_start":1,"char_count":16,"bounds":{"left":0.10538564,"top":0.06703911,"width":0.027925532,"height":0.011971269}}],"role_description":"text"},{"role":"AXStaticText","text":"⌘B","depth":16,"bounds":{"left":0.1349734,"top":0.06703911,"width":0.0063164895,"height":0.011971269},"on_screen":true,"role_description":"text"},{"role":"AXStaticText","text":"Drag to 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The burst window is per app; the daily quota is per portal.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". That's the limit the 429 in your example is hitting (","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"secondly limit","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"on","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/crm/v3/objects/contact/search","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"). It's a separate bucket from the 190/10s, and critically, search responses do not include the","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-*","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"headers","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— you can't read remaining capacity off the response, you have to track it yourself.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Non-search endpoints return","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Max","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", and","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Interval-Milliseconds","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". The daily headers are not returned for OAuth-authenticated calls","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— that's enforced for marketplace listing, but it's also a sane operational target.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"So you have","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"four buckets","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Where to store it","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Where to store it","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"INCR","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"EXPIRE","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"plus Lua scripting for true CAS semantics.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Key layout I'd use:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:search:{portalId} ZSET (sliding 1s)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:meta:{portalId} HASH (last seen headers)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The two short-window buckets are sliding-window-log (Redis ZSET, score =","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"microtime","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The daily bucket is a plain","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"INCR","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with TTL set to seconds-until-midnight in","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"HubSpot's account timezone","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to read it","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to read it","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Two-phase: optimistic pre-check, authoritative post-update.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Before a call, run the Lua acquire script. If it returns \"denied,\" sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"After the call, parse","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Max","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Interval-Milliseconds","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and store them in","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:meta:{portalId}","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"For search specifically, since headers don't come back, the local counter","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"is","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"not","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"on 429 (you really did make that request).","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to work with multiple jobs","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to work with multiple jobs","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Concurrency cap","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"via","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Queue::throttle()","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Per-tenant fairness","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Priority lanes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot-priority","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot-bulk","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Backoff on 429","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— respect the","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry-After","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"header. If absent, exponential with jitter (e.g.,","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"min(2^attempt * 250ms, 30s) ± 20%","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to work with paginated requests","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to work with paginated requests","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The trap is treating \"fetch all\" as one logical operation. Each page is its own API call and competes for tokens with everything else.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Two practical rules:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Don't hold the worker idle while paginating.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"processing","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"of page N as a separate job, and queue a \"fetch page N+1\" job with a delay equal to the wait time. Each page becomes its own atomic unit.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Always batch where HubSpot offers it.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/{type}/batch/{read|update|create}","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"accepts up to 100 IDs per call. For your \"patching IDs\" flow this is the difference between 600 calls and 6.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Walkthrough: 600 opportunities","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Walkthrough: 600 opportunities","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Sane flow:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Identify deltas","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— if you can avoid Search at all, do. Use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Batch read","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"current state —","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/deals/batch/read","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with 100 IDs per call → 6 calls. Burst weight 6.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Compute diff","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"in your code (no API).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Batch update","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"—","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/deals/batch/update","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with 100 per call → 6 calls.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Associations","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"—","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v4/associations/{from}/{to}/batch/create","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"if needed → another few batch calls.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Recognition: pre-check denies if","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"<weight>","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"slots aren't free; on 429 the response surfaces","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"policyName","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(DAILY/SECONDLY) — log it, push the job back with","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry-After","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"delay, increment a metric so you can alarm on sustained 429 rate.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Pitfalls & bottlenecks worth pre-empting","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Pitfalls & bottlenecks worth pre-empting","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Daily quota is shared across the whole portal","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search is uniquely fragile","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":": separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hs_object_id","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"ranges or by","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"windows.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry storms after HubSpot incidents","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Webhook → sync → webhook loops","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"jy_last_sync_id","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":") and ignore webhooks where it just changed.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Token refresh","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"expires_in - 60s","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", single-flight the refresh (only one worker refreshes; others wait on a lock).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search latency for fresh writes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Clock skew","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— Redis is authoritative. Don't use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"time()","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"from each worker for window scoring; use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"redis.call('TIME')","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"inside the Lua script.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Dead workers with held tokens","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The 5% error rule","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Phantom contention with Laravel","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"RateLimited","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"middleware","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":": it's per-job-class by default, not per-portal. If your","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"SyncDealJob","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"UpdateActivityJob","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"HubspotRateLimiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"service that everything routes through.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give positive feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give negative feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"You said: What will happen if there are two different types of rate limits?","depth":20,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"You said: What will happen if there are two different types of rate limits?","depth":21,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"What will happen if there are two different types of rate limits?","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search requests that have 5 requests per second","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"13:40","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Edit","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"Claude responded: They're independent buckets — with one shared bucket on top.","depth":21,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Claude responded: They're independent buckets — with one shared bucket on top.","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Analyzed independent rate limit buckets and their practical interactions","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Analyzed independent rate limit buckets and their practical interactions","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"They're independent buckets — with one shared bucket on top.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"The three buckets and what each call hits","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"The three buckets and what each call hits","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"┌──────────────────┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search request ──→ │ Search: 5/sec │ ──┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"└──────────────────┘ │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"├──→ ┌─────────────────┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"┌──────────────────┐ │ │ Daily: 250k–1M │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"└──────────────────┘ └─────────────────┘","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"What this means practically","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"What this means practically","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting search secondly limit (5/s):","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Only further","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/search","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting burst limit (190/10s):","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting daily limit:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"So can one type of request block the other?","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"So can one type of request block the other?","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search → Burst:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"No.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Burst → Search:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"No.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Saturating burst leaves Search's 5/s untouched.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Either → Daily:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Yes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", but only by contributing to the same daily counter. It's volume that kills you here, not the type.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"In theory you can sustain ~50 search + ~190 other =","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"~240 calls per 10 seconds","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"indefinitely (until daily runs out), because the two buckets bill independently.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Implementation consequence","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Implementation consequence","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"php","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'search'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// for search endpoints only","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'burst'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// for everything else","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'daily'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// every call, both kinds","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('search')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"+","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('daily')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". A batch update calls","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('burst')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"+","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('daily')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":".","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"One subtle pitfall: when you backoff on a 429,","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"inspect","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"policyName","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"in the response body","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to know which bucket actually fired. If you see","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"SECONDLY","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The other subtlety: the burst response gives you","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give positive feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give negative feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"You said: Ok, so what will happen each time I make a request?","depth":20,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"You said: Ok, so what will happen each time I make a request?","depth":21,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"13:44","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Edit","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.","depth":21,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Analyzed per-request overhead concerns and quantified Redis performance impact","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Analyzed per-request overhead concerns and quantified Redis performance impact","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out. But the overhead is much smaller than it sounds, and importantly, it's nowhere near your bottleneck.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"What \"two buckets per request\" actually costs","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"What \"two buckets per request\" actually costs","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Each request makes","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"one round trip to Redis","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— not two — because you fold both bucket checks into a single Lua script that runs atomically server-side:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"lua","depth":26,"on_screen":false,"role_description":"text"}]...
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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
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10:42
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Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
Copy
Give positive feedback
Give negative feedback
Retry
You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
Retry
Edit
Copy
Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
Copy to clipboard
php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.
Copy
Give positive feedback
Give negative feedback
Retry
You said: Ok, so what will happen each time I make a request?
You said: Ok, so what will happen each time I make a request?
Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?
13:44
Retry
Edit
Copy
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Analyzed per-request overhead concerns and quantified Redis performance impact
Analyzed per-request overhead concerns and quantified Redis performance impact
Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out. But the overhead is much smaller than it sounds, and importantly, it's nowhere near your bottleneck.
What "two buckets per request" actually costs
What "two buckets per request" actually costs
Each request makes
one round trip to Redis
— not two — because you fold both bucket checks into a single Lua script that runs atomically server-side:
Copy to clipboard
lua...
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PostmanEditVIewWindowmelp•• 0HubSpot rate limit i PostmanEditVIewWindowmelp•• 0HubSpot rate limit implementation strategyso you could theoretically push ~950k calis per portal per day without hitting any cap — burstIs une oniy unie uhrouling vou.whar vou sul need to wackThis actually simplifies vour limiter design. For OAuth apps, you only need:TimitActionburstulolosPer-portal Redis sliding window (you already have this)Search S/secPer-portal Redis sliding window (you already have this)DailyNot applicable. Skip the bucket entirelyYou can drop the hubspot:rl:daily:(portalld; counter from your design. No dallybudgeting. no reconciliation. no midnight reset logic. The /account-info/v3/api-usage/daily/private-apps endpoint is irrelevant for your situation - it tracks private appsinstalled in the customer's portal. which is none of vour business.Two real constraints that aren't quotas but still matter1. The 5% error rule. Reauests resulting in an error response shouldn't exceed 5% of totaldaily requests: this is enforced for marketplace listing and certification. So while there's nodailv call cap. sustained 429s do count against vour marketplace standing. Your pre-checkthrottling is what keeps this clean. hubspot2. Polling minimum. Integrations that poll HubSpot for new or updated information arelimited to polling intervals of five minutes or more. Worth knowing if any flow doesperiodic checks rather than reacting to webhooks. HubSpotSo the chort version: ves no dailv limit for nublic O4uth anns. Burst (llo/los) and search i(5/sec) are the onlv buckets vou need to enforce per portal. Your limiter iust got simpler.v COLLECtIONs, Contactsv CRM Objectsv crm/vslobiects/obiect Twoe,> U batchv ooiect Id›> • associations/{to Object Type)GET Read20, An error occurred.eg. successful operationDEL Archive> PATCH Update>GET List> POST Filter, Sort, and Search CRM Objects> CRM Owners> CRM Pinelinec> Dealsv Engagements> D OLD ENGAGEMENTSGET list meetingsPOST search moditied companiesPOST search tackeGET read call> POST soarch callsGET list callsPOST meetings scheduledGET get meetinaPoSt aet link to tack> POST Create Contact with Association> HubsnotJournal & webhoooks v4post Get tokenGET det suoscriotionsPOST create subscrintiorGET Journal earliestGET Journal latestGeT httos:nuosoot-yGET next offsetPOST aet Token prodDEL DELETE CLIRCCDIDTION DEP DOPTAIICAMIDONMCNTC) spFcsELOWSConnect Git E Console 2 Termirhttos:/lapi.hubaoCNeR-JHaMxlZoiNd."Statusdatecontent-typecf-rayct-cache-statuscache-controlstrict-transnort-securitvaccocc-control.allow-crodentinldserver-timingx-hubspot-correlation-idy-hubenot-notfoundlwwnhonrradhlmmawx-hubspot-ratelimit-remainingx-hubspot-ratelimit-secondlv-remainingrenort-toThu. 07 May 2026 11:25•25 GMTwovt/htmlcharcotcica.0950.19f7fdd2hea988428-SOfDYNAMICmust-revalidate,no-cache,no-storemax-ade-31536000- includeSubDomains: preloadorigin, Accept-Encodinahcid:desc="019e022f-77c3-7883-a217-66912667639f" cfr.desc="9f7fdd2c02ef3402-IAD"999 2917 669169760610000Keen coing in Claude CodeSwitch to Claude Code and let Claude work directly in your repo,running and testing as it goes.Open Claude Codcreate summarv of reauest to get dailv. burst and search. I want to create a short documentationfor myself. AlsoOpus 4.7 Adaptive vClaude ic Aland can mako mictakoc Plosco double-chock cited courcac)hell# Support Daily - in 31 mXx Hubspot vYour team is now on the Free plan with 1 admin. You retain editing access and other members are read-only. View team permissions to see who can edit, or upgrade to restore collaboration.GET next off. • POST search •GET read ciNo environmentvCRM Obiects > crm/v3/obiects/{obiect Tvoel > (obiect Id) > Read) SaveToaseurl)) /crm/vs/objects/call/4801/1536580/properties=boby.internalmeetingnotescassociations=contacis,deals,companiesE Docs Params • Authorization• Headers 9 Body ScriptsSettingsCookiesQuery ParamsKeyDescriotionBulk Edit ..v propertiesboby.internalveeuingnotesA comma separated list or the properties to be returned inpropertesnameA comma separated list of the properties to be returned in<string>A comma separated list of object types to retrieve associalcompaniesA comma separated list of obiect types to retrieve associalfalsefalseWhether to return only results that have been archived,205825333040The name of a proverty whose values are unique for this olv associationscontacis. deals companiesDescriptionHeaders 21 lest Results404 Not Found • 162 ms • 1.2 KB • Cêe.g. save kesponse100% L2VAIlVariables in requestC baseUrlo token• All VarlablesInu / May 14-29:04f"endnoints".f("url"."httnc+|Ma nel.cloudflare com\renort|/v42c=oq20F0hENDQHit@cRe2VRe"success fraction":0.01."report_to"."cf-nel""max age":604800}Giobals Vault Took -- m=m...
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PostmanEditVIewWindowmelp•• 0HubSpot rate limit i PostmanEditVIewWindowmelp•• 0HubSpot rate limit implementation strategyso you could theoretically push ~950k calis per portal per day without hitting any cap — burstIs une oniy unie uhrouling vou.whar vou sul need to wackThis actually simplifies vour limiter design. For OAuth apps, you only need:TimitActionburstulolosPer-portal Redis sliding window (you already have this)Search S/secPer-portal Redis sliding window (you already have this)DailyNot applicable. Skip the bucket entirelyYou can drop the hubspot:rl:daily:(portalld; counter from your design. No dallybudgeting. no reconciliation. no midnight reset logic. The /account-info/v3/api-usage/daily/private-apps endpoint is irrelevant for your situation - it tracks private appsinstalled in the customer's portal. which is none of vour business.Two real constraints that aren't quotas but still matter1. The 5% error rule. Reauests resulting in an error response shouldn't exceed 5% of totaldaily requests: this is enforced for marketplace listing and certification. So while there's nodailv call cap. sustained 429s do count against vour marketplace standing. Your pre-checkthrottling is what keeps this clean. hubspot2. Polling minimum. Integrations that poll HubSpot for new or updated information arelimited to polling intervals of five minutes or more. Worth knowing if any flow doesperiodic checks rather than reacting to webhooks. HubSpotSo the chort version: ves no dailv limit for nublic O4uth anns. Burst (llo/los) and search i(5/sec) are the onlv buckets vou need to enforce per portal. Your limiter iust got simpler.v COLLECtIONs, Contactsv CRM Objectsv crm/vslobiects/obiect Twoe,> U batchv ooiect Id›> • associations/{to Object Type)GET Read20, An error occurred.eg. successful operationDEL Archive> PATCH Update>GET List> POST Filter, Sort, and Search CRM Objects> CRM Owners> CRM Pinelinec> Dealsv Engagements> D OLD ENGAGEMENTSGET list meetingsPOST search moditied companiesPOST search tackeGET read call> POST soarch callsGET list callsPOST meetings scheduledGET get meetinaPoSt aet link to tack> POST Create Contact with Association> HubsnotJournal & webhoooks v4post Get tokenGET det suoscriotionsPOST create subscrintiorGET Journal earliestGET Journal latestGeT httos:nuosoot-yGET next offsetPOST aet Token prodDEL DELETE CLIRCCDIDTION DEP DOPTAIICAMIDONMCNTC) spFcsELOWSConnect Git E Console 2 Termirhttos:/lapi.hubaoCNeR-JHaMxlZoiNd."Statusdatecontent-typecf-rayct-cache-statuscache-controlstrict-transnort-securitvaccocc-control.allow-crodentinldserver-timingx-hubspot-correlation-idy-hubenot-notfoundlwwnhonrradhlmmawx-hubspot-ratelimit-remainingx-hubspot-ratelimit-secondlv-remainingrenort-toThu. 07 May 2026 11:25•25 GMTwovt/htmlcharcotcica.0950.19f7fdd2hea988428-SOfDYNAMICmust-revalidate,no-cache,no-storemax-ade-31536000- includeSubDomains: preloadorigin, Accept-Encodinahcid:desc="019e022f-77c3-7883-a217-66912667639f" cfr.desc="9f7fdd2c02ef3402-IAD"999 2917 669169760610000Keen coing in Claude CodeSwitch to Claude Code and let Claude work directly in your repo,running and testing as it goes.Open Claude Codcreate summarv of reauest to get dailv. burst and search. I want to create a short documentationfor myself. AlsoOpus 4.7 Adaptive vClaude ic Aland can mako mictakoc Plosco double-chock cited courcac)hell# Support Daily - in 31 mXx Hubspot vYour team is now on the Free plan with 1 admin. You retain editing access and other members are read-only. View team permissions to see who can edit, or upgrade to restore collaboration.GET next off. • POST search •GET read ciNo environmentvCRM Obiects > crm/v3/obiects/{obiect Tvoel > (obiect Id) > Read) SaveToaseurl)) /crm/vs/objects/call/4801/1536580/properties=boby.internalmeetingnotescassociations=contacis,deals,companiesE Docs Params • Authorization• Headers 9 Body ScriptsSettingsCookiesQuery ParamsKeyDescriotionBulk Edit ..v propertiesboby.internalveeuingnotesA comma separated list or the properties to be returned inpropertesnameA comma separated list of the properties to be returned in<string>A comma separated list of object types to retrieve associalcompaniesA comma separated list of obiect types to retrieve associalfalsefalseWhether to return only results that have been archived,205825333040The name of a proverty whose values are unique for this olv associationscontacis. deals companiesDescriptionHeaders 21 lest Results404 Not Found • 162 ms • 1.2 KB • Cêe.g. save kesponse100% L2VAIlVariables in requestC baseUrlo token• All VarlablesInu / May 14-29:04f"endnoints".f("url"."httnc+|Ma nel.cloudflare com\renort|/v42c=oq20F0hENDQHit@cRe2VRe"success fraction":0.01."report_to"."cf-nel""max age":604800}Giobals Vault Took -- m=m...
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PhpStorm
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faVsco.js – HS_local [jiminny@localhost]
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Project: faVsco.js, menu
master, menu
Start Listen Project: faVsco.js, menu
master, menu
Start Listening for PHP Debug Connections
AskJiminnyReportActivityServiceTest
Run 'AskJiminnyReportActivityServiceTest'
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# [PASSWORD_DOTS] HS [PASSWORD_DOTS]
select * from teams where id = 2; # 2
select * from features; # 2
select * from team_features where team_id = 2; # 2
select * from crm_configurations where id = 2; # 2
select * from users where team_id = 2; #
select * from playbooks where team_id = 2; # event 38
select * from playbook_categories where playbook_id = 38; #
SELECT * FROM activities WHERE crm_configuration_id = 2 and crm_provider_id is not null order by id desc;
[URL_WITH_CREDENTIALS] RateLimitInterface $rateLimit */
foreach ($provider->getRateLimits() as $rateLimit) {
$key = $rateLimit->getKey();
if ($this->rateLimiter->tooManyAttempts($key, $rateLimit->getQuota())) {
return false;
}
}
return true;
}
public function requestAvailableIn(RateLimited $provider): int
{
return $provider->getRateLimits()->isNotEmpty()
? $provider->getRateLimits()
->map(fn (RateLimitInterface $rateLimit): int => $this->rateLimiter->availableIn($rateLimit->getKey()))
->max()
: 0
;
}
public function incrementRequestCount(RateLimited $provider): void
{
/** @var RateLimitInterface $rateLimit */
foreach ($provider->getRateLimits() as $rateLimit) {
$this->rateLimiter->hit($rateLimit->getKey(), $rateLimit->getWindow());
}
}
}
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changed:","depth":4,"bounds":{"left":0.27027926,"top":1.0,"width":0.042220745,"height":0.0},"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Hide","depth":4,"bounds":{"left":0.27027926,"top":1.0,"width":0.008643617,"height":0.0},"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"6","depth":4,"bounds":{"left":0.6399601,"top":0.123703115,"width":0.007978723,"height":0.015163607},"on_screen":true,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":4,"bounds":{"left":0.6499335,"top":0.123703115,"width":0.00731383,"height":0.015163607},"on_screen":true,"role_description":"text"},{"role":"AXStaticText","text":"6","depth":4,"bounds":{"left":0.65924203,"top":0.123703115,"width":0.007978723,"height":0.015163607},"on_screen":true,"role_description":"text"},{"role":"AXButton","text":"Previous Highlighted Error","depth":4,"bounds":{"left":0.66888297,"top":0.12210695,"width":0.00731383,"height":0.018355945},"on_screen":true,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Next Highlighted Error","depth":4,"bounds":{"left":0.6761968,"top":0.12210695,"width":0.006981383,"height":0.018355945},"on_screen":true,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXTextArea","text":"# **************************** HS **************************************\n\nselect * from teams where id = 2; # 2\nselect * from features; # 2\nselect * from team_features where team_id = 2; # 2\nselect * from crm_configurations where id = 2; # 2\nselect * from users where team_id = 2; #\nselect * from playbooks where team_id = 2; # event 38\nselect * from playbook_categories where playbook_id = 38; #\n\nSELECT * FROM activities WHERE crm_configuration_id = 2 and crm_provider_id is not null order by id desc;\nhttps://app.hubspot.com/contacts/4392066/deal/16964514951/?engagement=96069102624\n https://app.staging.jiminny.com/playback/d5df34dc-bd66-4ff5-a7b3-8d3be30322a0\n\nSELECT * FROM activities WHERE uuid_to_bin('04fdcd0d-818f-4c53-92dc-6f18bc753ffd') = uuid;\n# 609126 softphone tr. 11241\n\nSELECT * FROM activities WHERE uuid_to_bin('6521bfcd-5a30-46e5-9f74-5440fd48befd') = uuid;\n# 608874 conference tr. 11226 crmId: 103422236596\n\nselect * from ai_prompts where transcription_id IN (11241, 11226);\nselect * from activity_summary_logs where activity_id = 608874;\n\nselect * from sidekick_settings;\nselect * from default_activity_types;\n\nselect * from crm_field_data where activity_id = 1223;\n\nselect * from crm_layouts where crm_configuration_id = 2;\nSELECT * FROM crm_layout_entities WHERE crm_layout_id IN (554);\nselect * from crm_fields where crm_configuration_id = 11 and object_type = 'event';\nSELECT * FROM crm_field_values WHERE crm_field_id IN (1455,1450);\n\nSELECT * FROM crm_field_data WHERE crm_layout_entity_id = 971;\nSELECT * FROM crm_field_data WHERE crm_layout_entity_id IN (6494,6495,6496,6497,6498,6499);\n\nSELECT\n CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,\n u.email,\n sa.*,\n t.owner_id FROM social_accounts sa\nJOIN users u\n on u.id = sa.sociable_id\nJOIN teams t on t.id = u.team_id\nWHERE u.team_id = 2 and sa.provider = 'hubspot';\n\nselect * from social_accounts where id = 1499;\n\nselect * from opportunities where team_id = 2\nand crm_provider_id IN ('51317301383');\n\nselect * from contacts where id = 85;\n\nselect * from opportunities where team_id = 2 order by id desc;\nselect * from opportunities where team_id = 2 and crm_provider_id = '51317301383'; # 5112\nselect * from opportunities where team_id = 2 and crm_provider_id = '55976759904'; # 5112\nselect * from opportunity_contacts where opportunity_id = 5117;\nselect * from crm_field_data where object_id = 1365;\nSELECT * FROM crm_fields WHERE id IN (1405, 1407, 1972, 2128);\n\nselect * from features;\nselect * from team_features where team_id IN (1);\nselect * from team_features where feature_id IN (36);\n\nSHOW CREATE TABLE opportunity_contacts;\nSELECT * FROM opportunity_contacts WHERE crm_provider_id = '111751';\n\n# $slug = 'HUBSPOT_WEBHOOK_SYNC';\n# $team = Jiminny\\Models\\Team::find(2);\n# $feature = Feature::query()->where('slug', $slug)->first();\n# TeamFeature::query()->create(['feature_id' => $feature->getId(),'team_id' => $team->getId()]);\n\n# hubspot_webhook_metrics\n\nselect * from opportunities where team_id = 2 and crm_provider_id IN ('374720564','14527423589','49908861993','50435771779'); # 1365\nSELECT * FROM opportunity_contacts WHERE opportunity_id = '414';\nSELECT * FROM opportunity_contacts WHERE crm_provider_id = '131501';\nselect * from contacts where id in (414, 464);\n\nselect * from activities where crm_configuration_id = 2;\n\nselect settings from crm_configurations where id = 11;\n\nselect * from teams; # 1, 2\nselect * from users;\nselect * from crm_configurations where id = 39;\nselect * from team_features where team_id = 2;\nselect * from features;\n# SELECT * FROM opportunities WHERE crm_configuration_id = 2\n# order by id desc;\n# and crm_provider_id = '49908861993';\n\n\nselect * from activity_providers where id IN (443, 202, 203, 227);\n\nselect * from activity_imports where id = 795889;\n\nselect c.id, c.provider, c.settings, t.* from teams t join crm_configurations c on t.id = c.team_id\nwhere c.provider = 'hubspot';\n\nselect * from crm_configurations crm JOIN teams t on crm.team_id = t.id\nwhere provider = 'hubspot';\nSELECT * FROM teams WHERE id = 31;\nSELECT * FROM users WHERE id = 257;\nSELECT * FROM opportunities WHERE team_id = 2;\n\nselect * from opportunity_contacts where opportunity_id = 5124;\nselect * from contacts where id IN (3850,3853,3851,4073,4140,4155,4480,4530,4623,5986,513,687,1806,1523,3613)\n\nselect * from activities where crm_configuration_id = 13;\n\nSELECT * FROM activities WHERE uuid_to_bin('826619ce-ec8e-4e59-8467-a01f5f6ad71e') = uuid; # 418141\n\n\nselect id, team_id, crm_provider_id from crm_configurations where provider = 'hubspot' and crm_provider_id IS NOT NULL;\nSELECT * FROM accounts WHERE team_id = 2 and crm_provider_id = '1212213464' order by id desc;\nSELECT * FROM contacts WHERE team_id = 2 and account_id = 5189 order by id desc;\nSELECT * FROM contacts WHERE team_id = 2 order by id desc;\nselect * from opportunity_contacts where contact_id = 6223;\nSELECT * FROM opportunities WHERE team_id = 2 and account_id = 5189 order by id desc;\n\nselect * from crm_profiles where crm_configuration_id = 2;\n\nselect * from activities where account_id = 46;","depth":4,"on_screen":true,"value":"# **************************** HS **************************************\n\nselect * from teams where id = 2; # 2\nselect * from features; # 2\nselect * from team_features where team_id = 2; # 2\nselect * from crm_configurations where id = 2; # 2\nselect * from users where team_id = 2; #\nselect * from playbooks where team_id = 2; # event 38\nselect * from playbook_categories where playbook_id = 38; #\n\nSELECT * FROM activities WHERE crm_configuration_id = 2 and crm_provider_id is not null order by id desc;\nhttps://app.hubspot.com/contacts/4392066/deal/16964514951/?engagement=96069102624\n https://app.staging.jiminny.com/playback/d5df34dc-bd66-4ff5-a7b3-8d3be30322a0\n\nSELECT * FROM activities WHERE uuid_to_bin('04fdcd0d-818f-4c53-92dc-6f18bc753ffd') = uuid;\n# 609126 softphone tr. 11241\n\nSELECT * FROM activities WHERE uuid_to_bin('6521bfcd-5a30-46e5-9f74-5440fd48befd') = uuid;\n# 608874 conference tr. 11226 crmId: 103422236596\n\nselect * from ai_prompts where transcription_id IN (11241, 11226);\nselect * from activity_summary_logs where activity_id = 608874;\n\nselect * from sidekick_settings;\nselect * from default_activity_types;\n\nselect * from crm_field_data where activity_id = 1223;\n\nselect * from crm_layouts where crm_configuration_id = 2;\nSELECT * FROM crm_layout_entities WHERE crm_layout_id IN (554);\nselect * from crm_fields where crm_configuration_id = 11 and object_type = 'event';\nSELECT * FROM crm_field_values WHERE crm_field_id IN (1455,1450);\n\nSELECT * FROM crm_field_data WHERE crm_layout_entity_id = 971;\nSELECT * FROM crm_field_data WHERE crm_layout_entity_id IN (6494,6495,6496,6497,6498,6499);\n\nSELECT\n CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,\n u.email,\n sa.*,\n t.owner_id FROM social_accounts sa\nJOIN users u\n on u.id = sa.sociable_id\nJOIN teams t on t.id = u.team_id\nWHERE u.team_id = 2 and sa.provider = 'hubspot';\n\nselect * from social_accounts where id = 1499;\n\nselect * from opportunities where team_id = 2\nand crm_provider_id IN ('51317301383');\n\nselect * from contacts where id = 85;\n\nselect * from opportunities where team_id = 2 order by id desc;\nselect * from opportunities where team_id = 2 and crm_provider_id = '51317301383'; # 5112\nselect * from opportunities where team_id = 2 and crm_provider_id = '55976759904'; # 5112\nselect * from opportunity_contacts where opportunity_id = 5117;\nselect * from crm_field_data where object_id = 1365;\nSELECT * FROM crm_fields WHERE id IN (1405, 1407, 1972, 2128);\n\nselect * from features;\nselect * from team_features where team_id IN (1);\nselect * from team_features where feature_id IN (36);\n\nSHOW CREATE TABLE opportunity_contacts;\nSELECT * FROM opportunity_contacts WHERE crm_provider_id = '111751';\n\n# $slug = 'HUBSPOT_WEBHOOK_SYNC';\n# $team = Jiminny\\Models\\Team::find(2);\n# $feature = Feature::query()->where('slug', $slug)->first();\n# TeamFeature::query()->create(['feature_id' => $feature->getId(),'team_id' => $team->getId()]);\n\n# hubspot_webhook_metrics\n\nselect * from opportunities where team_id = 2 and crm_provider_id IN ('374720564','14527423589','49908861993','50435771779'); # 1365\nSELECT * FROM opportunity_contacts WHERE opportunity_id = '414';\nSELECT * FROM opportunity_contacts WHERE crm_provider_id = '131501';\nselect * from contacts where id in (414, 464);\n\nselect * from activities where crm_configuration_id = 2;\n\nselect settings from crm_configurations where id = 11;\n\nselect * from teams; # 1, 2\nselect * from users;\nselect * from crm_configurations where id = 39;\nselect * from team_features where team_id = 2;\nselect * from features;\n# SELECT * FROM opportunities WHERE crm_configuration_id = 2\n# order by id desc;\n# and crm_provider_id = '49908861993';\n\n\nselect * from activity_providers where id IN (443, 202, 203, 227);\n\nselect * from activity_imports where id = 795889;\n\nselect c.id, c.provider, c.settings, t.* from teams t join crm_configurations c on t.id = c.team_id\nwhere c.provider = 'hubspot';\n\nselect * from crm_configurations crm JOIN teams t on crm.team_id = t.id\nwhere provider = 'hubspot';\nSELECT * FROM teams WHERE id = 31;\nSELECT * FROM users WHERE id = 257;\nSELECT * FROM opportunities WHERE team_id = 2;\n\nselect * from opportunity_contacts where opportunity_id = 5124;\nselect * from contacts where id IN (3850,3853,3851,4073,4140,4155,4480,4530,4623,5986,513,687,1806,1523,3613)\n\nselect * from activities where crm_configuration_id = 13;\n\nSELECT * FROM activities WHERE uuid_to_bin('826619ce-ec8e-4e59-8467-a01f5f6ad71e') = uuid; # 418141\n\n\nselect id, team_id, crm_provider_id from crm_configurations where provider = 'hubspot' and crm_provider_id IS NOT NULL;\nSELECT * FROM accounts WHERE team_id = 2 and crm_provider_id = '1212213464' order by id desc;\nSELECT * FROM contacts WHERE team_id = 2 and account_id = 5189 order by id desc;\nSELECT * FROM contacts WHERE team_id = 2 order by id desc;\nselect * from opportunity_contacts where contact_id = 6223;\nSELECT * FROM opportunities WHERE team_id = 2 and account_id = 5189 order by id desc;\n\nselect * from crm_profiles where crm_configuration_id = 2;\n\nselect * from activities where account_id = 46;","role_description":"text entry area","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Sync Changes","depth":4,"bounds":{"left":0.27027926,"top":1.0,"width":0.008643617,"height":0.0},"on_screen":false,"role_description":"button","is_enabled":false,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Hide This Notification","depth":4,"bounds":{"left":0.27027926,"top":1.0,"width":0.008643617,"height":0.0},"on_screen":false,"role_description":"button","is_enabled":false,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Code changed:","depth":4,"bounds":{"left":0.27027926,"top":1.0,"width":0.042220745,"height":0.0},"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Hide","depth":4,"bounds":{"left":0.27027926,"top":1.0,"width":0.008643617,"height":0.0},"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXTextArea","text":"<?php\n\ndeclare(strict_types=1);\n\nnamespace Jiminny\\Component\\Utility\\Service;\n\nuse Illuminate\\Cache\\RateLimiter;\nuse Jiminny\\Contracts\\Http\\RateLimited;\nuse Jiminny\\Contracts\\Http\\RateLimitInterface;\n\nclass ProviderRateLimiter\n{\n protected RateLimiter $rateLimiter;\n\n public function __construct(RateLimiter $rateLimiter)\n {\n $this->rateLimiter = $rateLimiter;\n }\n\n public function canMakeRequest(RateLimited $provider): bool\n {\n /** @var RateLimitInterface $rateLimit */\n foreach ($provider->getRateLimits() as $rateLimit) {\n $key = $rateLimit->getKey();\n\n if ($this->rateLimiter->tooManyAttempts($key, $rateLimit->getQuota())) {\n return false;\n }\n }\n\n return true;\n }\n\n public function requestAvailableIn(RateLimited $provider): int\n {\n return $provider->getRateLimits()->isNotEmpty()\n ? $provider->getRateLimits()\n ->map(fn (RateLimitInterface $rateLimit): int => $this->rateLimiter->availableIn($rateLimit->getKey()))\n ->max()\n : 0\n ;\n }\n\n public function incrementRequestCount(RateLimited $provider): void\n {\n /** @var RateLimitInterface $rateLimit */\n foreach ($provider->getRateLimits() as $rateLimit) {\n $this->rateLimiter->hit($rateLimit->getKey(), $rateLimit->getWindow());\n }\n }\n}","depth":4,"bounds":{"left":0.11968085,"top":0.0,"width":0.26163563,"height":0.7725459},"on_screen":true,"value":"<?php\n\ndeclare(strict_types=1);\n\nnamespace Jiminny\\Component\\Utility\\Service;\n\nuse Illuminate\\Cache\\RateLimiter;\nuse Jiminny\\Contracts\\Http\\RateLimited;\nuse Jiminny\\Contracts\\Http\\RateLimitInterface;\n\nclass ProviderRateLimiter\n{\n protected RateLimiter $rateLimiter;\n\n public function __construct(RateLimiter $rateLimiter)\n {\n $this->rateLimiter = $rateLimiter;\n }\n\n public function canMakeRequest(RateLimited $provider): bool\n {\n /** @var RateLimitInterface $rateLimit */\n foreach ($provider->getRateLimits() as $rateLimit) {\n $key = $rateLimit->getKey();\n\n if ($this->rateLimiter->tooManyAttempts($key, $rateLimit->getQuota())) {\n return false;\n }\n }\n\n return true;\n }\n\n public function requestAvailableIn(RateLimited $provider): int\n {\n return $provider->getRateLimits()->isNotEmpty()\n ? $provider->getRateLimits()\n ->map(fn (RateLimitInterface $rateLimit): int => $this->rateLimiter->availableIn($rateLimit->getKey()))\n ->max()\n : 0\n ;\n }\n\n public function incrementRequestCount(RateLimited $provider): void\n {\n /** @var RateLimitInterface $rateLimit */\n foreach ($provider->getRateLimits() as $rateLimit) {\n $this->rateLimiter->hit($rateLimit->getKey(), $rateLimit->getWindow());\n }\n }\n}","role_description":"text entry area","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Project","depth":3,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Project","depth":3,"bounds":{"left":0.011968086,"top":0.047885075,"width":0.024268618,"height":0.024740623},"on_screen":true,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"New File or Directory…","depth":4,"bounds":{"left":0.27027926,"top":1.0,"width":0.008643617,"height":0.0},"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Expand Selected","depth":4,"bounds":{"left":0.27027926,"top":1.0,"width":0.008643617,"height":0.0},"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Collapse All","depth":4,"bounds":{"left":0.27027926,"top":1.0,"width":0.008643617,"height":0.0},"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Options","depth":4,"bounds":{"left":0.27027926,"top":1.0,"width":0.008643617,"height":0.0},"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Hide","depth":4,"bounds":{"left":0.27027926,"top":1.0,"width":0.008643617,"height":0.0},"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false}]...
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-8281684270685400090
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2074680154977123917
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visual_change
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accessibility
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NULL
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jiminny
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# [PASSWORD_DOTS] HS [PASSWORD_DOTS]
select * from teams where id = 2; # 2
select * from features; # 2
select * from team_features where team_id = 2; # 2
select * from crm_configurations where id = 2; # 2
select * from users where team_id = 2; #
select * from playbooks where team_id = 2; # event 38
select * from playbook_categories where playbook_id = 38; #
SELECT * FROM activities WHERE crm_configuration_id = 2 and crm_provider_id is not null order by id desc;
[URL_WITH_CREDENTIALS] RateLimitInterface $rateLimit */
foreach ($provider->getRateLimits() as $rateLimit) {
$key = $rateLimit->getKey();
if ($this->rateLimiter->tooManyAttempts($key, $rateLimit->getQuota())) {
return false;
}
}
return true;
}
public function requestAvailableIn(RateLimited $provider): int
{
return $provider->getRateLimits()->isNotEmpty()
? $provider->getRateLimits()
->map(fn (RateLimitInterface $rateLimit): int => $this->rateLimiter->availableIn($rateLimit->getKey()))
->max()
: 0
;
}
public function incrementRequestCount(RateLimited $provider): void
{
/** @var RateLimitInterface $rateLimit */
foreach ($provider->getRateLimits() as $rateLimit) {
$this->rateLimiter->hit($rateLimit->getKey(), $rateLimit->getWindow());
}
}
}
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2026-05-07T11:29:58.220993+00:00
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/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778153398220_m2.jpg...
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Project: faVsco.js, menu
master, menu
Start Listen Project: faVsco.js, menu
master, menu
Start Listening for PHP Debug Connections
AskJiminnyReportActivityServiceTest
Run 'AskJiminnyReportActivityServiceTest'
Debug 'AskJiminnyReportActivityServiceTest'
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# [PASSWORD_DOTS] HS [PASSWORD_DOTS]
select * from teams where id = 2; # 2
select * from features; # 2
select * from team_features where team_id = 2; # 2
select * from crm_configurations where id = 2; # 2
select * from users where team_id = 2; #
select * from playbooks where team_id = 2; # event 38
select * from playbook_categories where playbook_id = 38; #
SELECT * FROM activities WHERE crm_configuration_id = 2 and crm_provider_id is not null order by id desc;
[URL_WITH_CREDENTIALS] RateLimitInterface $rateLimit */
foreach ($provider->getRateLimits() as $rateLimit) {
$key = $rateLimit->getKey();
if ($this->rateLimiter->tooManyAttempts($key, $rateLimit->getQuota())) {
return false;
}
}
return true;
}
public function requestAvailableIn(RateLimited $provider): int
{
return $provider->getRateLimits()->isNotEmpty()
? $provider->getRateLimits()
->map(fn (RateLimitInterface $rateLimit): int => $this->rateLimiter->availableIn($rateLimit->getKey()))
->max()
: 0
;
}
public function incrementRequestCount(RateLimited $provider): void
{
/** @var RateLimitInterface $rateLimit */
foreach ($provider->getRateLimits() as $rateLimit) {
$this->rateLimiter->hit($rateLimit->getKey(), $rateLimit->getWindow());
}
}
}...
|
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Highlighted Error","depth":4,"bounds":{"left":0.66888297,"top":0.12210695,"width":0.00731383,"height":0.018355945},"on_screen":true,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Next Highlighted Error","depth":4,"bounds":{"left":0.6761968,"top":0.12210695,"width":0.006981383,"height":0.018355945},"on_screen":true,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXTextArea","text":"# **************************** HS **************************************\n\nselect * from teams where id = 2; # 2\nselect * from features; # 2\nselect * from team_features where team_id = 2; # 2\nselect * from crm_configurations where id = 2; # 2\nselect * from users where team_id = 2; #\nselect * from playbooks where team_id = 2; # event 38\nselect * from playbook_categories where playbook_id = 38; #\n\nSELECT * FROM activities WHERE crm_configuration_id = 2 and crm_provider_id is not null order by id desc;\nhttps://app.hubspot.com/contacts/4392066/deal/16964514951/?engagement=96069102624\n https://app.staging.jiminny.com/playback/d5df34dc-bd66-4ff5-a7b3-8d3be30322a0\n\nSELECT * FROM activities WHERE uuid_to_bin('04fdcd0d-818f-4c53-92dc-6f18bc753ffd') = uuid;\n# 609126 softphone tr. 11241\n\nSELECT * FROM activities WHERE uuid_to_bin('6521bfcd-5a30-46e5-9f74-5440fd48befd') = uuid;\n# 608874 conference tr. 11226 crmId: 103422236596\n\nselect * from ai_prompts where transcription_id IN (11241, 11226);\nselect * from activity_summary_logs where activity_id = 608874;\n\nselect * from sidekick_settings;\nselect * from default_activity_types;\n\nselect * from crm_field_data where activity_id = 1223;\n\nselect * from crm_layouts where crm_configuration_id = 2;\nSELECT * FROM crm_layout_entities WHERE crm_layout_id IN (554);\nselect * from crm_fields where crm_configuration_id = 11 and object_type = 'event';\nSELECT * FROM crm_field_values WHERE crm_field_id IN (1455,1450);\n\nSELECT * FROM crm_field_data WHERE crm_layout_entity_id = 971;\nSELECT * FROM crm_field_data WHERE crm_layout_entity_id IN (6494,6495,6496,6497,6498,6499);\n\nSELECT\n CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,\n u.email,\n sa.*,\n t.owner_id FROM social_accounts sa\nJOIN users u\n on u.id = sa.sociable_id\nJOIN teams t on t.id = u.team_id\nWHERE u.team_id = 2 and sa.provider = 'hubspot';\n\nselect * from social_accounts where id = 1499;\n\nselect * from opportunities where team_id = 2\nand crm_provider_id IN ('51317301383');\n\nselect * from contacts where id = 85;\n\nselect * from opportunities where team_id = 2 order by id desc;\nselect * from opportunities where team_id = 2 and crm_provider_id = '51317301383'; # 5112\nselect * from opportunities where team_id = 2 and crm_provider_id = '55976759904'; # 5112\nselect * from opportunity_contacts where opportunity_id = 5117;\nselect * from crm_field_data where object_id = 1365;\nSELECT * FROM crm_fields WHERE id IN (1405, 1407, 1972, 2128);\n\nselect * from features;\nselect * from team_features where team_id IN (1);\nselect * from team_features where feature_id IN (36);\n\nSHOW CREATE TABLE opportunity_contacts;\nSELECT * FROM opportunity_contacts WHERE crm_provider_id = '111751';\n\n# $slug = 'HUBSPOT_WEBHOOK_SYNC';\n# $team = Jiminny\\Models\\Team::find(2);\n# $feature = Feature::query()->where('slug', $slug)->first();\n# TeamFeature::query()->create(['feature_id' => $feature->getId(),'team_id' => $team->getId()]);\n\n# hubspot_webhook_metrics\n\nselect * from opportunities where team_id = 2 and crm_provider_id IN ('374720564','14527423589','49908861993','50435771779'); # 1365\nSELECT * FROM opportunity_contacts WHERE opportunity_id = '414';\nSELECT * FROM opportunity_contacts WHERE crm_provider_id = '131501';\nselect * from contacts where id in (414, 464);\n\nselect * from activities where crm_configuration_id = 2;\n\nselect settings from crm_configurations where id = 11;\n\nselect * from teams; # 1, 2\nselect * from users;\nselect * from crm_configurations where id = 39;\nselect * from team_features where team_id = 2;\nselect * from features;\n# SELECT * FROM opportunities WHERE crm_configuration_id = 2\n# order by id desc;\n# and crm_provider_id = '49908861993';\n\n\nselect * from activity_providers where id IN (443, 202, 203, 227);\n\nselect * from activity_imports where id = 795889;\n\nselect c.id, c.provider, c.settings, t.* from teams t join crm_configurations c on t.id = c.team_id\nwhere c.provider = 'hubspot';\n\nselect * from crm_configurations crm JOIN teams t on crm.team_id = t.id\nwhere provider = 'hubspot';\nSELECT * FROM teams WHERE id = 31;\nSELECT * FROM users WHERE id = 257;\nSELECT * FROM opportunities WHERE team_id = 2;\n\nselect * from opportunity_contacts where opportunity_id = 5124;\nselect * from contacts where id IN 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id = 2; # 2\nselect * from features; # 2\nselect * from team_features where team_id = 2; # 2\nselect * from crm_configurations where id = 2; # 2\nselect * from users where team_id = 2; #\nselect * from playbooks where team_id = 2; # event 38\nselect * from playbook_categories where playbook_id = 38; #\n\nSELECT * FROM activities WHERE crm_configuration_id = 2 and crm_provider_id is not null order by id desc;\nhttps://app.hubspot.com/contacts/4392066/deal/16964514951/?engagement=96069102624\n https://app.staging.jiminny.com/playback/d5df34dc-bd66-4ff5-a7b3-8d3be30322a0\n\nSELECT * FROM activities WHERE uuid_to_bin('04fdcd0d-818f-4c53-92dc-6f18bc753ffd') = uuid;\n# 609126 softphone tr. 11241\n\nSELECT * FROM activities WHERE uuid_to_bin('6521bfcd-5a30-46e5-9f74-5440fd48befd') = uuid;\n# 608874 conference tr. 11226 crmId: 103422236596\n\nselect * from ai_prompts where transcription_id IN (11241, 11226);\nselect * from activity_summary_logs where activity_id = 608874;\n\nselect * from sidekick_settings;\nselect * from default_activity_types;\n\nselect * from crm_field_data where activity_id = 1223;\n\nselect * from crm_layouts where crm_configuration_id = 2;\nSELECT * FROM crm_layout_entities WHERE crm_layout_id IN (554);\nselect * from crm_fields where crm_configuration_id = 11 and object_type = 'event';\nSELECT * FROM crm_field_values WHERE crm_field_id IN (1455,1450);\n\nSELECT * FROM crm_field_data WHERE crm_layout_entity_id = 971;\nSELECT * FROM crm_field_data WHERE crm_layout_entity_id IN (6494,6495,6496,6497,6498,6499);\n\nSELECT\n CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,\n u.email,\n sa.*,\n t.owner_id FROM social_accounts sa\nJOIN users u\n on u.id = sa.sociable_id\nJOIN teams t on t.id = u.team_id\nWHERE u.team_id = 2 and sa.provider = 'hubspot';\n\nselect * from social_accounts where id = 1499;\n\nselect * from opportunities where team_id = 2\nand crm_provider_id IN ('51317301383');\n\nselect * from contacts where id = 85;\n\nselect * from opportunities where team_id = 2 order by id desc;\nselect * from opportunities where team_id = 2 and crm_provider_id = '51317301383'; # 5112\nselect * from opportunities where team_id = 2 and crm_provider_id = '55976759904'; # 5112\nselect * from opportunity_contacts where opportunity_id = 5117;\nselect * from crm_field_data where object_id = 1365;\nSELECT * FROM crm_fields WHERE id IN (1405, 1407, 1972, 2128);\n\nselect * from features;\nselect * from team_features where team_id IN (1);\nselect * from team_features where feature_id IN (36);\n\nSHOW CREATE TABLE opportunity_contacts;\nSELECT * FROM opportunity_contacts WHERE crm_provider_id = '111751';\n\n# $slug = 'HUBSPOT_WEBHOOK_SYNC';\n# $team = Jiminny\\Models\\Team::find(2);\n# $feature = Feature::query()->where('slug', $slug)->first();\n# TeamFeature::query()->create(['feature_id' => $feature->getId(),'team_id' => $team->getId()]);\n\n# hubspot_webhook_metrics\n\nselect * from opportunities where 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account_id = 5189 order by id desc;\n\nselect * from crm_profiles where crm_configuration_id = 2;\n\nselect * from activities where account_id = 46;","role_description":"text entry area","is_enabled":true,"is_focused":true,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Sync Changes","depth":4,"bounds":{"left":0.27027926,"top":1.0,"width":0.008643617,"height":0.0},"on_screen":false,"role_description":"button","is_enabled":false,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Hide This Notification","depth":4,"bounds":{"left":0.27027926,"top":1.0,"width":0.008643617,"height":0.0},"on_screen":false,"role_description":"button","is_enabled":false,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Code changed:","depth":4,"bounds":{"left":0.27027926,"top":1.0,"width":0.042220745,"height":0.0},"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Hide","depth":4,"bounds":{"left":0.27027926,"top":1.0,"width":0.008643617,"height":0.0},"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXTextArea","text":"<?php\n\ndeclare(strict_types=1);\n\nnamespace Jiminny\\Component\\Utility\\Service;\n\nuse Illuminate\\Cache\\RateLimiter;\nuse Jiminny\\Contracts\\Http\\RateLimited;\nuse Jiminny\\Contracts\\Http\\RateLimitInterface;\n\nclass ProviderRateLimiter\n{\n protected RateLimiter $rateLimiter;\n\n public function __construct(RateLimiter $rateLimiter)\n {\n $this->rateLimiter = $rateLimiter;\n }\n\n public function canMakeRequest(RateLimited $provider): bool\n {\n /** @var RateLimitInterface $rateLimit */\n foreach ($provider->getRateLimits() as $rateLimit) {\n $key = $rateLimit->getKey();\n\n if ($this->rateLimiter->tooManyAttempts($key, $rateLimit->getQuota())) {\n return false;\n }\n }\n\n return true;\n }\n\n public function requestAvailableIn(RateLimited $provider): int\n {\n return $provider->getRateLimits()->isNotEmpty()\n ? $provider->getRateLimits()\n ->map(fn (RateLimitInterface $rateLimit): int => $this->rateLimiter->availableIn($rateLimit->getKey()))\n ->max()\n : 0\n ;\n }\n\n public function incrementRequestCount(RateLimited $provider): void\n {\n /** @var RateLimitInterface $rateLimit */\n foreach ($provider->getRateLimits() as $rateLimit) {\n $this->rateLimiter->hit($rateLimit->getKey(), $rateLimit->getWindow());\n }\n }\n}","depth":4,"bounds":{"left":0.11968085,"top":0.1963288,"width":0.26163563,"height":0.782921},"on_screen":true,"value":"<?php\n\ndeclare(strict_types=1);\n\nnamespace Jiminny\\Component\\Utility\\Service;\n\nuse Illuminate\\Cache\\RateLimiter;\nuse Jiminny\\Contracts\\Http\\RateLimited;\nuse Jiminny\\Contracts\\Http\\RateLimitInterface;\n\nclass ProviderRateLimiter\n{\n protected RateLimiter $rateLimiter;\n\n public function __construct(RateLimiter $rateLimiter)\n {\n $this->rateLimiter = $rateLimiter;\n }\n\n public function canMakeRequest(RateLimited $provider): bool\n {\n /** @var RateLimitInterface $rateLimit */\n foreach ($provider->getRateLimits() as $rateLimit) {\n $key = $rateLimit->getKey();\n\n if ($this->rateLimiter->tooManyAttempts($key, $rateLimit->getQuota())) {\n return false;\n }\n }\n\n return true;\n }\n\n public function requestAvailableIn(RateLimited $provider): int\n {\n return $provider->getRateLimits()->isNotEmpty()\n ? $provider->getRateLimits()\n ->map(fn (RateLimitInterface $rateLimit): int => $this->rateLimiter->availableIn($rateLimit->getKey()))\n ->max()\n : 0\n ;\n }\n\n public function incrementRequestCount(RateLimited $provider): void\n {\n /** @var RateLimitInterface $rateLimit */\n foreach ($provider->getRateLimits() as $rateLimit) {\n $this->rateLimiter->hit($rateLimit->getKey(), $rateLimit->getWindow());\n }\n }\n}","role_description":"text entry area","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false}]...
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2074680154977123933
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click
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accessibility
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NULL
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Project: faVsco.js, menu
master, menu
Start Listen Project: faVsco.js, menu
master, menu
Start Listening for PHP Debug Connections
AskJiminnyReportActivityServiceTest
Run 'AskJiminnyReportActivityServiceTest'
Debug 'AskJiminnyReportActivityServiceTest'
More Actions
JetBrains AI
Search Everywhere
IDE and Project Settings
Execute
Explain Plan
Browse Query History
View Parameters
Open Query Execution Settings…
In-Editor Results
Tx: Auto
Cancel Running Statements
Playground
jiminny
Sync Changes
Hide This Notification
Code changed:
Hide
6
1
6
Previous Highlighted Error
Next Highlighted Error
# [PASSWORD_DOTS] HS [PASSWORD_DOTS]
select * from teams where id = 2; # 2
select * from features; # 2
select * from team_features where team_id = 2; # 2
select * from crm_configurations where id = 2; # 2
select * from users where team_id = 2; #
select * from playbooks where team_id = 2; # event 38
select * from playbook_categories where playbook_id = 38; #
SELECT * FROM activities WHERE crm_configuration_id = 2 and crm_provider_id is not null order by id desc;
[URL_WITH_CREDENTIALS] RateLimitInterface $rateLimit */
foreach ($provider->getRateLimits() as $rateLimit) {
$key = $rateLimit->getKey();
if ($this->rateLimiter->tooManyAttempts($key, $rateLimit->getQuota())) {
return false;
}
}
return true;
}
public function requestAvailableIn(RateLimited $provider): int
{
return $provider->getRateLimits()->isNotEmpty()
? $provider->getRateLimits()
->map(fn (RateLimitInterface $rateLimit): int => $this->rateLimiter->availableIn($rateLimit->getKey()))
->max()
: 0
;
}
public function incrementRequestCount(RateLimited $provider): void
{
/** @var RateLimitInterface $rateLimit */
foreach ($provider->getRateLimits() as $rateLimit) {
$this->rateLimiter->hit($rateLimit->getKey(), $rateLimit->getWindow());
}
}
}...
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2587
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NULL
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NULL
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NULL
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2590
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110
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7
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2026-05-07T11:30:00.512902+00:00
|
/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778153400512_m2.jpg...
|
PhpStorm
|
faVsco.js – HS_local [jiminny@localhost]
|
True
|
NULL
|
monitor_2
|
NULL
|
NULL
|
NULL
|
NULL
|
Project: faVsco.js, menu
master, menu
Start Listen Project: faVsco.js, menu
master, menu
Start Listening for PHP Debug Connections
AskJiminnyReportActivityServiceTest
Run 'AskJiminnyReportActivityServiceTest'
Debug 'AskJiminnyReportActivityServiceTest'
More Actions
JetBrains AI
Search Everywhere
IDE and Project Settings
Execute
Explain Plan
Browse Query History
View Parameters
Open Query Execution Settings…
In-Editor Results
Tx: Auto
Cancel Running Statements
Playground
jiminny
Sync Changes
Hide This Notification
Code changed:
Hide
6
1
6
Previous Highlighted Error
Next Highlighted Error
# [PASSWORD_DOTS] HS [PASSWORD_DOTS]
select * from teams where id = 2; # 2
select * from features; # 2
select * from team_features where team_id = 2; # 2
select * from crm_configurations where id = 2; # 2
select * from users where team_id = 2; #
select * from playbooks where team_id = 2; # event 38
select * from playbook_categories where playbook_id = 38; #
SELECT * FROM activities WHERE crm_configuration_id = 2 and crm_provider_id is not null order by id desc;
[URL_WITH_CREDENTIALS] RateLimitInterface $rateLimit */
foreach ($provider->getRateLimits() as $rateLimit) {
$key = $rateLimit->getKey();
if ($this->rateLimiter->tooManyAttempts($key, $rateLimit->getQuota())) {
return false;
}
}
return true;
}
public function requestAvailableIn(RateLimited $provider): int
{
return $provider->getRateLimits()->isNotEmpty()
? $provider->getRateLimits()
->map(fn (RateLimitInterface $rateLimit): int => $this->rateLimiter->availableIn($rateLimit->getKey()))
->max()
: 0
;
}
public function incrementRequestCount(RateLimited $provider): void
{
/** @var RateLimitInterface $rateLimit */
foreach ($provider->getRateLimits() as $rateLimit) {
$this->rateLimiter->hit($rateLimit->getKey(), $rateLimit->getWindow());
}
}
}
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Highlighted Error","depth":4,"bounds":{"left":0.66888297,"top":0.12210695,"width":0.00731383,"height":0.018355945},"on_screen":true,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Next Highlighted Error","depth":4,"bounds":{"left":0.6761968,"top":0.12210695,"width":0.006981383,"height":0.018355945},"on_screen":true,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXTextArea","text":"# **************************** HS **************************************\n\nselect * from teams where id = 2; # 2\nselect * from features; # 2\nselect * from team_features where team_id = 2; # 2\nselect * from crm_configurations where id = 2; # 2\nselect * from users where team_id = 2; #\nselect * from playbooks where team_id = 2; # event 38\nselect * from playbook_categories where playbook_id = 38; #\n\nSELECT * FROM activities WHERE crm_configuration_id = 2 and crm_provider_id is not null order by id desc;\nhttps://app.hubspot.com/contacts/4392066/deal/16964514951/?engagement=96069102624\n https://app.staging.jiminny.com/playback/d5df34dc-bd66-4ff5-a7b3-8d3be30322a0\n\nSELECT * FROM activities WHERE uuid_to_bin('04fdcd0d-818f-4c53-92dc-6f18bc753ffd') = uuid;\n# 609126 softphone tr. 11241\n\nSELECT * FROM activities WHERE uuid_to_bin('6521bfcd-5a30-46e5-9f74-5440fd48befd') = uuid;\n# 608874 conference tr. 11226 crmId: 103422236596\n\nselect * from ai_prompts where transcription_id IN (11241, 11226);\nselect * from activity_summary_logs where activity_id = 608874;\n\nselect * from sidekick_settings;\nselect * from default_activity_types;\n\nselect * from crm_field_data where activity_id = 1223;\n\nselect * from crm_layouts where crm_configuration_id = 2;\nSELECT * FROM crm_layout_entities WHERE crm_layout_id IN (554);\nselect * from crm_fields where crm_configuration_id = 11 and object_type = 'event';\nSELECT * FROM crm_field_values WHERE crm_field_id IN (1455,1450);\n\nSELECT * FROM crm_field_data WHERE crm_layout_entity_id = 971;\nSELECT * FROM crm_field_data WHERE crm_layout_entity_id IN (6494,6495,6496,6497,6498,6499);\n\nSELECT\n CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,\n u.email,\n sa.*,\n t.owner_id FROM social_accounts sa\nJOIN users u\n on u.id = sa.sociable_id\nJOIN teams t on t.id = u.team_id\nWHERE u.team_id = 2 and sa.provider = 'hubspot';\n\nselect * from social_accounts where id = 1499;\n\nselect * from opportunities where team_id = 2\nand crm_provider_id IN ('51317301383');\n\nselect * from contacts where id = 85;\n\nselect * from opportunities where team_id = 2 order by id desc;\nselect * from opportunities where team_id = 2 and crm_provider_id = '51317301383'; # 5112\nselect * from opportunities where team_id = 2 and crm_provider_id = '55976759904'; # 5112\nselect * from opportunity_contacts where opportunity_id = 5117;\nselect * from crm_field_data where object_id = 1365;\nSELECT * FROM crm_fields WHERE id IN (1405, 1407, 1972, 2128);\n\nselect * from features;\nselect * from team_features where team_id IN (1);\nselect * from team_features where feature_id IN (36);\n\nSHOW CREATE TABLE opportunity_contacts;\nSELECT * FROM opportunity_contacts WHERE crm_provider_id = '111751';\n\n# $slug = 'HUBSPOT_WEBHOOK_SYNC';\n# $team = Jiminny\\Models\\Team::find(2);\n# $feature = Feature::query()->where('slug', $slug)->first();\n# TeamFeature::query()->create(['feature_id' => $feature->getId(),'team_id' => $team->getId()]);\n\n# hubspot_webhook_metrics\n\nselect * from opportunities where team_id = 2 and crm_provider_id IN ('374720564','14527423589','49908861993','50435771779'); # 1365\nSELECT * FROM opportunity_contacts WHERE opportunity_id = '414';\nSELECT * FROM opportunity_contacts WHERE crm_provider_id = '131501';\nselect * from contacts where id in (414, 464);\n\nselect * from activities where crm_configuration_id = 2;\n\nselect settings from crm_configurations where id = 11;\n\nselect * from teams; # 1, 2\nselect * from users;\nselect * from crm_configurations where id = 39;\nselect * from team_features where team_id = 2;\nselect * from features;\n# SELECT * FROM opportunities WHERE crm_configuration_id = 2\n# order by id desc;\n# and crm_provider_id = '49908861993';\n\n\nselect * from activity_providers where id IN (443, 202, 203, 227);\n\nselect * from activity_imports where id = 795889;\n\nselect c.id, c.provider, c.settings, t.* from teams t join crm_configurations c on t.id = c.team_id\nwhere c.provider = 'hubspot';\n\nselect * from crm_configurations crm JOIN teams t on crm.team_id = t.id\nwhere provider = 'hubspot';\nSELECT * FROM teams WHERE id = 31;\nSELECT * FROM users WHERE id = 257;\nSELECT * FROM opportunities WHERE team_id = 2;\n\nselect * from opportunity_contacts where opportunity_id = 5124;\nselect * from contacts where id IN 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id = 2; # 2\nselect * from features; # 2\nselect * from team_features where team_id = 2; # 2\nselect * from crm_configurations where id = 2; # 2\nselect * from users where team_id = 2; #\nselect * from playbooks where team_id = 2; # event 38\nselect * from playbook_categories where playbook_id = 38; #\n\nSELECT * FROM activities WHERE crm_configuration_id = 2 and crm_provider_id is not null order by id desc;\nhttps://app.hubspot.com/contacts/4392066/deal/16964514951/?engagement=96069102624\n https://app.staging.jiminny.com/playback/d5df34dc-bd66-4ff5-a7b3-8d3be30322a0\n\nSELECT * FROM activities WHERE uuid_to_bin('04fdcd0d-818f-4c53-92dc-6f18bc753ffd') = uuid;\n# 609126 softphone tr. 11241\n\nSELECT * FROM activities WHERE uuid_to_bin('6521bfcd-5a30-46e5-9f74-5440fd48befd') = uuid;\n# 608874 conference tr. 11226 crmId: 103422236596\n\nselect * from ai_prompts where transcription_id IN (11241, 11226);\nselect * from activity_summary_logs where activity_id = 608874;\n\nselect * from sidekick_settings;\nselect * from default_activity_types;\n\nselect * from crm_field_data where activity_id = 1223;\n\nselect * from crm_layouts where crm_configuration_id = 2;\nSELECT * FROM crm_layout_entities WHERE crm_layout_id IN (554);\nselect * from crm_fields where crm_configuration_id = 11 and object_type = 'event';\nSELECT * FROM crm_field_values WHERE crm_field_id IN (1455,1450);\n\nSELECT * FROM crm_field_data WHERE crm_layout_entity_id = 971;\nSELECT * FROM crm_field_data WHERE crm_layout_entity_id IN (6494,6495,6496,6497,6498,6499);\n\nSELECT\n CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,\n u.email,\n sa.*,\n t.owner_id FROM social_accounts sa\nJOIN users u\n on u.id = sa.sociable_id\nJOIN teams t on t.id = u.team_id\nWHERE u.team_id = 2 and sa.provider = 'hubspot';\n\nselect * from social_accounts where id = 1499;\n\nselect * from opportunities where team_id = 2\nand crm_provider_id IN ('51317301383');\n\nselect * from contacts where id = 85;\n\nselect * from opportunities where team_id = 2 order by id desc;\nselect * from opportunities where team_id = 2 and crm_provider_id = '51317301383'; # 5112\nselect * from opportunities where team_id = 2 and crm_provider_id = '55976759904'; # 5112\nselect * from opportunity_contacts where opportunity_id = 5117;\nselect * from crm_field_data where object_id = 1365;\nSELECT * FROM crm_fields WHERE id IN (1405, 1407, 1972, 2128);\n\nselect * from features;\nselect * from team_features where team_id IN (1);\nselect * from team_features where feature_id IN (36);\n\nSHOW CREATE TABLE opportunity_contacts;\nSELECT * FROM opportunity_contacts WHERE crm_provider_id = '111751';\n\n# $slug = 'HUBSPOT_WEBHOOK_SYNC';\n# $team = Jiminny\\Models\\Team::find(2);\n# $feature = Feature::query()->where('slug', $slug)->first();\n# TeamFeature::query()->create(['feature_id' => $feature->getId(),'team_id' => $team->getId()]);\n\n# hubspot_webhook_metrics\n\nselect * from opportunities where team_id = 2 and crm_provider_id IN ('374720564','14527423589','49908861993','50435771779'); # 1365\nSELECT * FROM opportunity_contacts WHERE opportunity_id = '414';\nSELECT * FROM opportunity_contacts WHERE crm_provider_id = '131501';\nselect * from contacts where id in (414, 464);\n\nselect * from activities where crm_configuration_id = 2;\n\nselect settings from crm_configurations where id = 11;\n\nselect * from teams; # 1, 2\nselect * from users;\nselect * from crm_configurations where id = 39;\nselect * from team_features where team_id = 2;\nselect * from features;\n# SELECT * FROM opportunities WHERE crm_configuration_id = 2\n# order by id desc;\n# and crm_provider_id = '49908861993';\n\n\nselect * from activity_providers where id IN (443, 202, 203, 227);\n\nselect * from activity_imports where id = 795889;\n\nselect c.id, c.provider, c.settings, t.* from teams t join crm_configurations c on t.id = c.team_id\nwhere c.provider = 'hubspot';\n\nselect * from crm_configurations crm JOIN teams t on crm.team_id = t.id\nwhere provider = 'hubspot';\nSELECT * FROM teams WHERE id = 31;\nSELECT * FROM users WHERE id = 257;\nSELECT * FROM opportunities WHERE team_id = 2;\n\nselect * from opportunity_contacts where opportunity_id = 5124;\nselect * from contacts where id IN (3850,3853,3851,4073,4140,4155,4480,4530,4623,5986,513,687,1806,1523,3613)\n\nselect * from activities where crm_configuration_id = 13;\n\nSELECT * FROM activities WHERE uuid_to_bin('826619ce-ec8e-4e59-8467-a01f5f6ad71e') = uuid; # 418141\n\n\nselect id, team_id, crm_provider_id from crm_configurations where provider = 'hubspot' and crm_provider_id IS NOT NULL;\nSELECT * FROM accounts WHERE team_id = 2 and crm_provider_id = '1212213464' order by id desc;\nSELECT * FROM contacts WHERE team_id = 2 and account_id = 5189 order by id desc;\nSELECT * FROM contacts WHERE team_id = 2 order by id desc;\nselect * from opportunity_contacts where contact_id = 6223;\nSELECT * FROM opportunities WHERE team_id = 2 and account_id = 5189 order by id desc;\n\nselect * from crm_profiles where crm_configuration_id = 2;\n\nselect * from activities where account_id = 46;","role_description":"text entry area","is_enabled":true,"is_focused":true,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Sync Changes","depth":4,"bounds":{"left":0.27027926,"top":1.0,"width":0.008643617,"height":0.0},"on_screen":false,"role_description":"button","is_enabled":false,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Hide This Notification","depth":4,"bounds":{"left":0.27027926,"top":1.0,"width":0.008643617,"height":0.0},"on_screen":false,"role_description":"button","is_enabled":false,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Code changed:","depth":4,"bounds":{"left":0.27027926,"top":1.0,"width":0.042220745,"height":0.0},"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Hide","depth":4,"bounds":{"left":0.27027926,"top":1.0,"width":0.008643617,"height":0.0},"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXTextArea","text":"<?php\n\ndeclare(strict_types=1);\n\nnamespace Jiminny\\Component\\Utility\\Service;\n\nuse Illuminate\\Cache\\RateLimiter;\nuse Jiminny\\Contracts\\Http\\RateLimited;\nuse Jiminny\\Contracts\\Http\\RateLimitInterface;\n\nclass ProviderRateLimiter\n{\n protected RateLimiter $rateLimiter;\n\n public function __construct(RateLimiter $rateLimiter)\n {\n $this->rateLimiter = $rateLimiter;\n }\n\n public function canMakeRequest(RateLimited $provider): bool\n {\n /** @var RateLimitInterface $rateLimit */\n foreach ($provider->getRateLimits() as $rateLimit) {\n $key = $rateLimit->getKey();\n\n if ($this->rateLimiter->tooManyAttempts($key, $rateLimit->getQuota())) {\n return false;\n }\n }\n\n return true;\n }\n\n public function requestAvailableIn(RateLimited $provider): int\n {\n return $provider->getRateLimits()->isNotEmpty()\n ? $provider->getRateLimits()\n ->map(fn (RateLimitInterface $rateLimit): int => $this->rateLimiter->availableIn($rateLimit->getKey()))\n ->max()\n : 0\n ;\n }\n\n public function incrementRequestCount(RateLimited $provider): void\n {\n /** @var RateLimitInterface $rateLimit */\n foreach ($provider->getRateLimits() as $rateLimit) {\n $this->rateLimiter->hit($rateLimit->getKey(), $rateLimit->getWindow());\n }\n }\n}","depth":4,"bounds":{"left":0.11968085,"top":0.1963288,"width":0.26163563,"height":0.782921},"on_screen":true,"value":"<?php\n\ndeclare(strict_types=1);\n\nnamespace Jiminny\\Component\\Utility\\Service;\n\nuse Illuminate\\Cache\\RateLimiter;\nuse Jiminny\\Contracts\\Http\\RateLimited;\nuse Jiminny\\Contracts\\Http\\RateLimitInterface;\n\nclass ProviderRateLimiter\n{\n protected RateLimiter $rateLimiter;\n\n public function __construct(RateLimiter $rateLimiter)\n {\n $this->rateLimiter = $rateLimiter;\n }\n\n public function canMakeRequest(RateLimited $provider): bool\n {\n /** @var RateLimitInterface $rateLimit */\n foreach ($provider->getRateLimits() as $rateLimit) {\n $key = $rateLimit->getKey();\n\n if ($this->rateLimiter->tooManyAttempts($key, $rateLimit->getQuota())) {\n return false;\n }\n }\n\n return true;\n }\n\n public function requestAvailableIn(RateLimited $provider): int\n {\n return $provider->getRateLimits()->isNotEmpty()\n ? $provider->getRateLimits()\n ->map(fn (RateLimitInterface $rateLimit): int => $this->rateLimiter->availableIn($rateLimit->getKey()))\n ->max()\n : 0\n ;\n }\n\n public function incrementRequestCount(RateLimited $provider): void\n {\n /** @var RateLimitInterface $rateLimit */\n foreach ($provider->getRateLimits() as $rateLimit) {\n $this->rateLimiter->hit($rateLimit->getKey(), $rateLimit->getWindow());\n }\n }\n}","role_description":"text entry area","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Project","depth":3,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Project","depth":3,"bounds":{"left":0.011968086,"top":0.047885075,"width":0.024268618,"height":0.024740623},"on_screen":true,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"New File or Directory…","depth":4,"bounds":{"left":0.27027926,"top":1.0,"width":0.008643617,"height":0.0},"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Expand Selected","depth":4,"bounds":{"left":0.27027926,"top":1.0,"width":0.008643617,"height":0.0},"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Collapse All","depth":4,"bounds":{"left":0.27027926,"top":1.0,"width":0.008643617,"height":0.0},"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Options","depth":4,"bounds":{"left":0.27027926,"top":1.0,"width":0.008643617,"height":0.0},"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Hide","depth":4,"bounds":{"left":0.27027926,"top":1.0,"width":0.008643617,"height":0.0},"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false}]...
|
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|
2074680154977123917
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visual_change
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accessibility
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NULL
|
Project: faVsco.js, menu
master, menu
Start Listen Project: faVsco.js, menu
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# [PASSWORD_DOTS] HS [PASSWORD_DOTS]
select * from teams where id = 2; # 2
select * from features; # 2
select * from team_features where team_id = 2; # 2
select * from crm_configurations where id = 2; # 2
select * from users where team_id = 2; #
select * from playbooks where team_id = 2; # event 38
select * from playbook_categories where playbook_id = 38; #
SELECT * FROM activities WHERE crm_configuration_id = 2 and crm_provider_id is not null order by id desc;
[URL_WITH_CREDENTIALS] RateLimitInterface $rateLimit */
foreach ($provider->getRateLimits() as $rateLimit) {
$key = $rateLimit->getKey();
if ($this->rateLimiter->tooManyAttempts($key, $rateLimit->getQuota())) {
return false;
}
}
return true;
}
public function requestAvailableIn(RateLimited $provider): int
{
return $provider->getRateLimits()->isNotEmpty()
? $provider->getRateLimits()
->map(fn (RateLimitInterface $rateLimit): int => $this->rateLimiter->availableIn($rateLimit->getKey()))
->max()
: 0
;
}
public function incrementRequestCount(RateLimited $provider): void
{
/** @var RateLimitInterface $rateLimit */
foreach ($provider->getRateLimits() as $rateLimit) {
$this->rateLimiter->hit($rateLimit->getKey(), $rateLimit->getWindow());
}
}
}
Project
Project
New File or Directory…
Expand Selected
Collapse All
Options
Hide...
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NULL
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NULL
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NULL
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NULL
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|
2592
|
110
|
8
|
2026-05-07T11:30:02.413279+00:00
|
/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778153402413_m2.jpg...
|
PhpStorm
|
faVsco.js – Hubspot/Client.php
|
True
|
NULL
|
monitor_2
|
NULL
|
NULL
|
NULL
|
NULL
|
Project: faVsco.js, menu
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Start Listening for PHP Debug Connections
AskJiminnyReportActivityServiceTest
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# [PASSWORD_DOTS] HS [PASSWORD_DOTS]
select * from teams where id = 2; # 2
select * from features; # 2
select * from team_features where team_id = 2; # 2
select * from crm_configurations where id = 2; # 2
select * from users where team_id = 2; #
select * from playbooks where team_id = 2; # event 38
select * from playbook_categories where playbook_id = 38; #
SELECT * FROM activities WHERE crm_configuration_id = 2 and crm_provider_id is not null order by id desc;
[URL_WITH_CREDENTIALS] CrmFieldOption array{id:string, label:string, value?:string}
*/
class Client extends BaseClient implements HubspotClientInterface
{
public const string MIN_API_VERSION = '2';
public const string BASE_URL = '[URL_WITH_CREDENTIALS] T
* @param callable(): T $apiCall
* @return T
*
* @throws RateLimitException
*/
private function executeRequest(callable $apiCall)
{
if (! $this->rateLimiter->canMakeRequest($this->config)) {
$retryAfter = $this->rateLimiter->requestAvailableIn($this->config);
$this->log->warning('[Hubspot] Rate limit exceeded, deferring request', [
'team_id' => $this->config->team_id,
'config_id' => $this->config->getId(),
'retry_after' => $retryAfter,
]);
throw new RateLimitException(
'Hubspot rate limit reached for configuration ' . $this->config->getId(),
$retryAfter,
);
}
$this->rateLimiter->incrementRequestCount($this->config);
try {
return $apiCall();
} catch (Throwable $e) {
if ($this->isHubspotRateLimit($e)) {
$retryAfter = $this->parseRetryAfter($e);
$this->log->warning('[Hubspot] Received 429 from API', [
'team_id' => $this->config->team_id,
'config_id' => $this->config->getId(),
'retry_after' => $retryAfter,
'reason' => $e->getMessage(),
]);
throw new RateLimitException('Hubspot returned 429', $retryAfter, $e);
}
throw $e;
}
}
private function isHubspotRateLimit(Throwable $e): bool
{
return method_exists($e, 'getCode') && (int) $e->getCode() === 429;
}
private function parseRetryAfter(Throwable $e): int
{
if (method_exists($e, 'getResponseHeaders')) {
$headers = $e->getResponseHeaders() ?: [];
$value = $headers['Retry-After'] ?? $headers['retry-after'] ?? null;
if (is_array($value)) {
$value = $value[0] ?? null;
}
if (is_numeric($value)) {
return (int) $value;
}
}
return 10;
}
public function getMinimumApiVersion(): string
{
return self::MIN_API_VERSION;
}
public function getInstance(): Factory
{
return new Factory([
'key' => $this->accessToken,
'oauth2' => true,
'base_url' => $this->baseUrl,
]);
}
public function getNewInstance(): Discovery
{
return \HubSpot\Factory::createWithAccessToken($this->accessToken);
}
/**
* Secondly and daily limits for Hubspot API
*
* Product Tier: Free & Starter | Professional & Enterprise | API add-on (any tier)
* Burst: 100/10 seconds | 150/10 seconds | 200/10 seconds
* Daily: 250,000 | 500,000 | 1,000,000
*
* Official documentation states: The search endpoints are rate limited to five requests per second.
* Since with 5 RPS were still hitting secondly rate limits we lowered it to 4
*/
public function getPaginatedData(array $payload, string $type, int $offset = 0): array
{
$total = 0;
$lastId = null;
$rows = [];
foreach ($this->getPaginatedDataGenerator($payload, $type, $offset, $total, $lastId) as $row) {
$rows[] = $row;
}
return ['results' => $rows, 'total' => $total, 'last_record' => $lastId];
}
/**
* @throws HubspotException
* @throws SocialAccountTokenInvalidException
* @throws BadRequest
*/
public function getPaginatedDataGenerator(
array $payload,
string $type,
int $offset = 0,
int &$total = 0,
?string &$lastRecordId = null
): \Generator {
return $this->paginationService->getPaginatedDataGenerator(
$this,
$payload,
$type,
$offset,
$total,
$lastRecordId
);
}
/**
* @throws DealApiException
* @throws CrmException
*/
public function getOpportunityById(string $crmId, array $fields): array
{
try {
$deal = $this->executeRequest(fn () => $this->getNewInstance()->crm()->deals()->basicApi()->getById(
$crmId,
implode(',', $fields),
'companies,contacts'
));
} catch (DealApiException $e) {
$this->log->info('[Hubspot] Failed to fetch opportunity', [
'crm_id' => $crmId,
'reason' => $e->getMessage(),
]);
throw $e;
}
if (! $deal instanceof DealWithAssociations) {
throw new CrmException('Deal not found');
}
return [
'id' => $deal->getId(),
'properties' => $deal->getProperties(),
'associations' => $deal->getAssociations(),
];
}
/**
* Generic batch read method for HubSpot objects
*
* @param string $objectType The object type ('deals', 'companies', 'contacts')
* @param array<string> $crmIds Array of HubSpot object IDs (max 100)
* @param array<string> $fields Array of property names to fetch
*
* @return array<string, array> Array keyed by CRM ID with object data
*/
private function batchReadObjects(string $objectType, array $crmIds, array $fields): array
{
if (empty($crmIds)) {
return [];
}
$this->validateBatchSize($objectType, $crmIds);
$this->ensureValidToken();
try {
$batchConfig = $this->createBatchConfiguration($objectType);
$batchReadRequest = $this->prepareBatchRequest($batchConfig, $crmIds, $fields);
$response = $batchConfig['api']->read($batchReadRequest);
$this->validateApiResponse($response, $objectType);
$results = $this->processApiResults($response);
$this->logBatchResults($objectType, $crmIds, $results);
return $results;
} catch (\Throwable $e) {
$this->handleBatchError($e, $objectType, $crmIds);
}
}
private function validateBatchSize(string $objectType, array $crmIds): void
{
if (count($crmIds) > 100) {
throw new \InvalidArgumentException("Batch size cannot exceed 100 {$objectType}");
}
}
private function createBatchConfiguration(string $objectType): array
{
$configurations = [
'deals' => [
'batchReadRequest' => new \HubSpot\Client\Crm\Deals\Model\BatchReadInputSimplePublicObjectId(),
'inputClass' => \HubSpot\Client\Crm\Deals\Model\SimplePublicObjectId::class,
'api' => $this->getNewInstance()->crm()->deals()->batchApi(),
],
'companies' => [
'batchReadRequest' => new \HubSpot\Client\Crm\Companies\Model\BatchReadInputSimplePublicObjectId(),
'inputClass' => \HubSpot\Client\Crm\Companies\Model\SimplePublicObjectId::class,
'api' => $this->getNewInstance()->crm()->companies()->batchApi(),
],
'contacts' => [
'batchReadRequest' => new \HubSpot\Client\Crm\Contacts\Model\BatchReadInputSimplePublicObjectId(),
'inputClass' => \HubSpot\Client\Crm\Contacts\Model\SimplePublicObjectId::class,
'api' => $this->getNewInstance()->crm()->contacts()->batchApi(),
],
];
if (! isset($configurations[$objectType])) {
throw new \InvalidArgumentException("Unsupported object type: {$objectType}");
}
return $configurations[$objectType];
}
private function prepareBatchRequest(array $batchConfig, array $crmIds, array $fields): object
{
$batchReadRequest = $batchConfig['batchReadRequest'];
$inputClass = $batchConfig['inputClass'];
$inputs = array_map(function ($crmId) use ($inputClass) {
$input = new $inputClass();
$input->setId($crmId);
return $input;
}, $crmIds);
$batchReadRequest->setInputs($inputs);
$batchReadRequest->setProperties($fields);
return $batchReadRequest;
}
private function validateApiResponse($response, string $objectType): void
{
if (! $response) {
throw new CrmException("HubSpot API returned null response for {$objectType} batch read");
}
}
private function processApiResults($response): array
{
$results = [];
$responseResults = $response->getResults();
if ($responseResults) {
foreach ($responseResults as $object) {
if ($object && $object->getId()) {
$results[$object->getId()] = [
'id' => $object->getId(),
'properties' => $object->getProperties() ?: [],
];
}
}
}
return $results;
}
private function logBatchResults(string $objectType, array $crmIds, array $results): void
{
$this->log->info("[HubSpot] Batch fetched {$objectType}", [
'requested_count' => count($crmIds),
'returned_count' => count($results),
'crm_ids' => $crmIds,
]);
}
private function handleBatchError(\Throwable $e, string $objectType, array $crmIds): void
{
$errorMessage = $e->getMessage() ?: 'Unknown error';
$errorTrace = $e->getTraceAsString() ?: 'No trace available';
$this->log->error("[HubSpot] Failed to batch fetch {$objectType}", [
'crm_ids' => $crmIds,
'error' => $errorMessage,
'trace' => $errorTrace,
]);
throw new CrmException("Failed to batch fetch {$objectType}: " . $errorMessage);
}
/**
* Batch read multiple opportunities by their CRM IDs
*
* @param array<string> $crmIds Array of HubSpot deal IDs (max 100)
* @param array<string> $fields Array of property names to fetch
*
* @return array<string, array> Array keyed by CRM ID with opportunity data
*/
public function getOpportunitiesByIds(array $crmIds, array $fields): array
{
return $this->batchReadObjects('deals', $crmIds, $fields);
}
/**
* Batch read multiple companies by their CRM IDs
*
* @param array<string> $crmIds Array of HubSpot company IDs (max 100)
* @param array<string> $fields Array of property names to fetch
*
* @return array<string, array> Array keyed by CRM ID with company data
*/
public function getCompaniesByIds(array $crmIds, array $fields): array
{
return $this->batchReadObjects('companies', $crmIds, $fields);
}
/**
* Batch read multiple contacts by their CRM IDs
*
* @param array<string> $crmIds Array of HubSpot contact IDs (max 100)
* @param array<string> $fields Array of property names to fetch
*
* @return array<string, array> Array keyed by CRM ID with contact data
*/
public function getContactsByIds(array $crmIds, array $fields): array
{
return $this->batchReadObjects('contacts', $crmIds, $fields);
}
/**
* @throws CompanyApiException
* @throws CrmException
*/
public function getAccountById(string $crmId, array $fields): array
{
try {
$company = $this->getNewInstance()->crm()->companies()->basicApi()->getById(
$crmId,
implode(',', $fields),
);
} catch (CompanyApiException $e) {
$this->log->info('[Hubspot] Failed to fetch account', [
'crm_id' => $crmId,
'reason' => $e->getMessage(),
]);
throw $e;
}
if (! $company instanceof CompaniesWithAssociations) {
throw new CrmException('Account not found');
}
return [
'id' => $company->getId(),
'properties' => $company->getProperties(),
];
}
/**
* @throws ContactApiException
* @throws CrmException
*/
public function getContactById(string $crmId, array $fields): array
{
try {
$contact = $this->getNewInstance()->crm()->contacts()->basicApi()->getById(
$crmId,
implode(',', $fields)
);
} catch (ContactApiException $e) {
$this->log->info('[Hubspot] Failed to fetch contact', [
'crm_id' => $crmId,
'reason' => $e->getMessage(),
]);
throw $e;
}
if (! $contact instanceof ContactsWithAssociations) {
throw new CrmException('Contact not found');
}
return [
'id' => $contact->getId(),
'properties' => $contact->getProperties(),
];
}
/**
* This is email search request that Hubspot offers as GET (more generous quota)
*/
public function getContactByEmail(string $email, array $fields = []): array
{
try {
$contact = $this->getNewInstance()->crm()->contacts()->basicApi()->getById(
$email,
implode(',', $fields),
null,
false,
'email'
);
return [
'id' => $contact->getId(),
'properties' => $contact->getProperties(),
];
} catch (ContactApiException $e) {
$this->log->info('[Hubspot] Failed to fetch contact', [
'email' => $email,
'reason' => $e->getMessage(),
]);
return [];
}
}
/**
* @throws CrmException
*/
public function fetchProperty(string $objectType, string $propertyId): Property
{
$result = $this->getNewInstance()->crm()->properties()->coreApi()->getByName($objectType, $propertyId);
if (! $result instanceof Property) {
$this->log->error('[Hubspot] Failed to fetch property', [
'object_type' => $objectType,
'property_id' => $propertyId,
'reason' => $result->getMessage(),
]);
throw new CrmException('Failed to fetch property');
}
return $result;
}
/**
* @return array<CrmFieldOption>
*/
public function fetchPropertyOptions(string $objectType, string $propertyId): array
{
/** @var array<CrmFieldOption> */
return $this->fetchProperty($objectType, $propertyId)->getOptions();
}
/**
* @return array<array{id:string, label:string, deleted:bool}>
*/
public function fetchCallDispositions(): array
{
/** @var Response $response */
$response = $this->getInstance()->engagements()->getCallDispositions();
/**
* @var array<array{
* id:string,
* label:string,
* deleted: bool
* }>
*/
return $response->toArray();
}
/**
* @return array<CrmFieldOption>
*/
public function fetchOpportunityPipelineStages(): array
{
$stages = [];
$apiResponse = $this->getNewInstance()->crm()->pipelines()->pipelinesApi()->getAll('deals');
if ($apiResponse instanceof Error) {
$this->log->error('[Hubspot] Failed to fetch opportunity pipelines', [
'reason' => $apiResponse->getMessage(),
]);
return [];
}
foreach ($apiResponse->getResults() as $pipeline) {
$pipelineStages = array_map(
static function (PipelineStage $stage) {
return [
'id' => $stage->getId(),
'label' => $stage->getLabel(),
];
},
$pipeline->getStages()
);
$stages = array_merge($stages, $pipelineStages);
}
return $stages;
}
public function fetchOpportunityPipelines(): array
{
$pipelines = [];
try {
$apiResponse = $this->makeRequest('/crm/v3/pipelines/deals');
} catch (\Exception $e) {
$this->log->info('[Hubspot] Failed to fetch opportunity pipelines', [
'reason' => $e->getMessage(),
]);
return [];
}
$response = $apiResponse->toArray();
foreach ($response['results'] as $pipeline) {
$pipelines[] = [
'id' => $pipeline['id'],
'label' => $pipeline['label'],
];
}
return $pipelines;
}
/**
* @return array<CrmFieldOption>
*/
public function fetchMeetingOutcomeFieldOptions(Field $field): array
{
return $field->getCrmProviderId() === 'meetingOutcome'
? $this->fetchMeetingOutcomeTypes()
: $this->fetchCallActivityTypes();
}
public function fetchMeetingOutcomeTypes(): array
{
return $this->extractMeetingTypeOptions(
'[URL_WITH_CREDENTIALS] Response $response */
$response = $this->getInstance()
->getClient()
->request('GET', $endpoint);
/**
* @var array<array{
* value: string,
* label: string,
* displayOrder: int
* }> $optionData
*/
$optionData = $response->toArray()['options'] ?? [];
$options = [];
foreach ($optionData as $item) {
$options[] = [
'id' => $item['value'],
'value' => $item['value'],
'label' => $item['label'],
'display_order' => $item['displayOrder'],
];
}
return $options;
}
/**
* @return array<CrmFieldOption>
*/
public function fetchDispositionFieldOptions(): array
{
$options = [];
$dispositions = $this->fetchCallDispositions();
foreach ($dispositions as $disposition) {
if ($disposition['deleted'] !== false) {
continue;
}
$option['value'] = $disposition['id'];
$option['id'] = $disposition['id'];
$option['label'] = $disposition['label'];
$options[] = $option;
}
return $options;
}
/**
* @return array<CrmFieldOption>
*/
public function fetchOpportunityFieldOptions(Field $field): array
{
if ($field->isStageField()) {
return $this->fetchOpportunityPipelineStages();
}
if ($field->isPipelineField()) {
return $this->fetchOpportunityPipelines();
}
return $this->fetchPropertyOptions('deals', $field->getCrmProviderId());
}
/**
* @throws BadRequest
* @throws HubspotException
*/
public function makeRequest(string $endpoint, $method = 'GET', $payload = [], ?string $queryString = null)
{
$endpoint = self::BASE_URL . $endpoint;
if ($method === 'GET') {
return $this->getInstance()->getClient()?->request(
method: $method,
endpoint: $endpoint,
query_string: $queryString
);
} else {
return $this->getInstance()->getClient()->request($method, $endpoint, [
'json' => ($payload),
]);
}
}
/**
* @throws BadRequest
* @throws HubspotException
*/
public function createMeeting(array $payload): Response
{
$endpoint = '/crm/v3/objects/meetings';
return $this->makeRequest($endpoint, 'POST', $payload);
}
/**
* @throws BadRequest
* @throws HubspotException
*/
public function updateMeeting(string $meetingId, array $payload): Response
{
$endpoint = '/crm/v3/objects/meetings/' . $meetingId;
return $this->makeRequest($endpoint, 'PATCH', $payload);
}
/**
* @throws \Exception
*/
public function createNote(
string $body,
string $ownerId,
int $timestamp,
string $objectId,
NoteObject $noteObject
): ?string {
try {
$noteInput = new SimplePublicObjectInput([
'properties' => [
'hs_note_body' => $body,
'hubspot_owner_id' => $ownerId,
'hs_timestamp' => $timestamp,
],
]);
// Create note
$note = $this->getNewInstance()->crm()->objects()->basicApi()->create('note', $noteInput);
$this->getNewInstance()->crm()->objects()->associationsApi()->create(
'note',
$note->getId(),
$this->getNoteObject($noteObject),
$objectId,
$this->getNoteAssociationType($noteObject),
);
return $note->getId();
} catch (\Exception $e) {
$this->log->error('[Hubspot] Failed to create note', [
'objectId' => $objectId,
'noteObject' => $noteObject->getObjectType(),
'reason' => $e->getMessage(),
]);
\Sentry::captureException($e);
}
return null;
}
public function updateEngagement(string $objectId, array $engagement, array $metadata): void
{
$this->getInstance()->engagements()->update($objectId, $engagement, $metadata);
}
public function getEngagementData(string $engagementId): array
{
$engagement = $this->getInstance()->engagements()->get($engagementId);
return $engagement->toArray();
}
public function createEngagement(array $engagement, array $associations, array $metadata): Response
{
return $this->getInstance()
->engagements()
->create($engagement, $associations, $metadata);
}
public function isUnauthorizedException(\Exception $e): bool
{
// Check for specific HubSpot API exception types first
if ($e instanceof BadRequest) {
// BadRequest can contain 401 status codes
return $e->getCode() === 401;
}
// Check for HTTP client exceptions with status codes
if ($e instanceof \GuzzleHttp\Exception\RequestException && $e->hasResponse()) {
$response = $e->getResponse();
if ($response !== null) {
return $response->getStatusCode() === 401;
}
}
// Check for Guzzle HTTP exceptions
if ($e instanceof \GuzzleHttp\Exception\ClientException) {
return $e->getCode() === 401;
}
// Fallback to string matching as last resort, but be more specific
$message = strtolower($e->getMessage());
return str_contains($message, '401 unauthorized') ||
str_contains($message, 'http 401') ||
str_contains($message, 'status code 401') ||
(preg_match('/\b401\b/', $message) && str_contains($message, 'unauthorized'));
}
/**
* Validates and refreshes the access token if needed before API requests.
* This ensures long-running processes don't fail due to token expiration.
*
* @throws SocialAccountTokenInvalidException
*/
public function ensureValidToken(): void
{
if ($this->oauthAccount === null) {
return;
}
$newToken = $this->tokenManager->ensureValidToken($this->oauthAccount);
if ($newToken !== null) {
$this->accessToken = $newToken;
}
}
public function getConfig()
{
return $this->config;
}
// returns only active (archived=false)
public function getOwners(): array
{
return $this->getNewInstance()->crm()->owners()->getAll();
}
/**
* @param bool $archived
*
* @return array<Owner>|[]
*/
public function getOwnersArchived(bool $archived = true): array
{
$endpoint = '/crm/v3/owners';
$queryParams = [
'archived' => $archived ? 'true' : 'false',
];
$queryString = http_build_query($queryParams);
$owners = [];
try {
$response = $this->makeRequest(endpoint: $endpoint, queryString: $queryString);
$responseData = $response?->toArray();
foreach ($responseData['results'] as $result) {
try {
$owners[] = Owner::create($result);
} catch (Throwable $e) {
$this->log->error('[HubSpot] Failed to process owner data', [
'result' => $result,
'error' => $e->getMessage(),
]);
continue;
}
}
} catch (Throwable $e) {
$this->log->error('HubSpot] Failed to fetch owners', [
'archived' => $archived,
'error' => $e->getMessage(),
]);
return [];
}
return $owners;
}
public function getMeeting(string $engagementId): ObjectWithAssociations
{
return $this->getNewInstance()->crm()->objects()->basicApi()
->getById('meeting', $engagementId, null, 'contact,company,deal');
}
public function deleteEngagement(string $engagementId): void
{
$this->getInstance()->engagements()->delete((int) $engagementId);
}
public function getAssociationsData(array $ids, string $fromObject, string $toObject): array
{
$associationData = [];
$idChunks = array_chunk($ids, self::ASSOCIATIONS_BATCH_SIZE_LIMIT);
foreach ($idChunks as $idChunk) {
try {
$batchInput = new \HubSpot\Client\Crm\Associations\Model\BatchInputPublicObjectId();
$batchInput->setInputs(array_map(function ($id) {
$publicObjectId = new \HubSpot\Client\Crm\Associations\Model\PublicObjectId();
$publicObjectId->setId($id);
return $publicObjectId;
}, $idChunk));
$associatedObjectsData = $this
->getNewInstance()
->crm()
->associations()
->batchApi()
->read($fromObject, $toObject, $batchInput);
if ($associatedObjectsData instanceof \HubSpot\Client\Crm\Associations\Model\BatchResponsePublicAssociationMulti) {
foreach ($associatedObjectsData->getResults() as $association) {
$from = $association->getFrom()->getId();
$toAssociations = $association->getTo();
if (! empty($toAssociations)) {
$associationData[$from] = array_map(function ($item) {
return $item->getId();
}, $toAssociations);
}
}
}
} catch (\Exception $e) {
$this->log->error('[Hubspot] Failed to fetch associations', [
'from_object' => $fromObject,
'to_object' => $toObject,
'reason' => $e->getMessage(),
]);
}
}
return $associationData;
}
/**
* @throws \Exception
*/
private function getNoteAssociationType(NoteObject $noteObject): string
{
return match($noteObject) {
NoteObject::Opportunity => 'note_to_deal',
NoteObject::Lead, NoteObject::Contact => 'note_to_contact', // or 'note_to_lead' if your portal supports it
NoteObject::Account => 'note_to_company',
NoteObject::Call, NoteObject::Event => throw new \Exception('Not supported'),
};
}
/**
* @throws \Exception
*/
private function getNoteObject(NoteObject $noteObject): string
{
return match($noteObject) {
NoteObject::Opportunity => 'deal',
NoteObject::Lead, NoteObject::Contact => 'contact',
NoteObject::Account => 'company',
NoteObject::Call, NoteObject::Event => throw new \Exception('Not supported'),
};
}
public function addAssociations(string $objectType, string $associationType, array $payload): Response
{
$endpoint = "/crm/v4/associations/$objectType/$associationType/batch/create";
return $this->makeRequest($endpoint, 'POST', $payload);
}
public function removeAssociations(string $objectType, string $associationType, array $payload): Response
{
$endpoint = "/crm/v4/associations/$objectType/$associationType/batch/archive";
return $this->makeRequest($endpoint, 'POST', $payload);
}
}
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crm_field_id IN (1455,1450);\n\nSELECT * FROM crm_field_data WHERE crm_layout_entity_id = 971;\nSELECT * FROM crm_field_data WHERE crm_layout_entity_id IN (6494,6495,6496,6497,6498,6499);\n\nSELECT\n CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,\n u.email,\n sa.*,\n t.owner_id FROM social_accounts sa\nJOIN users u\n on u.id = sa.sociable_id\nJOIN teams t on t.id = u.team_id\nWHERE u.team_id = 2 and sa.provider = 'hubspot';\n\nselect * from social_accounts where id = 1499;\n\nselect * from opportunities where team_id = 2\nand crm_provider_id IN ('51317301383');\n\nselect * from contacts where id = 85;\n\nselect * from opportunities where team_id = 2 order by id desc;\nselect * from opportunities where team_id = 2 and crm_provider_id = '51317301383'; # 5112\nselect * from opportunities where team_id = 2 and crm_provider_id = '55976759904'; # 5112\nselect * from opportunity_contacts where opportunity_id = 5117;\nselect * from crm_field_data where object_id = 1365;\nSELECT * FROM crm_fields WHERE id IN (1405, 1407, 1972, 2128);\n\nselect * from features;\nselect * from team_features where team_id IN (1);\nselect * from team_features where feature_id IN (36);\n\nSHOW CREATE TABLE opportunity_contacts;\nSELECT * FROM opportunity_contacts WHERE crm_provider_id = '111751';\n\n# $slug = 'HUBSPOT_WEBHOOK_SYNC';\n# $team = Jiminny\\Models\\Team::find(2);\n# $feature = Feature::query()->where('slug', $slug)->first();\n# TeamFeature::query()->create(['feature_id' => $feature->getId(),'team_id' => $team->getId()]);\n\n# hubspot_webhook_metrics\n\nselect * from opportunities where team_id = 2 and crm_provider_id IN ('374720564','14527423589','49908861993','50435771779'); # 1365\nSELECT * FROM opportunity_contacts WHERE opportunity_id = '414';\nSELECT * FROM opportunity_contacts WHERE crm_provider_id = '131501';\nselect * from contacts where id in (414, 464);\n\nselect * from activities where crm_configuration_id = 2;\n\nselect settings from crm_configurations where id = 11;\n\nselect * from teams; # 1, 2\nselect * from users;\nselect * from crm_configurations where id = 39;\nselect * from team_features where team_id = 2;\nselect * from features;\n# SELECT * FROM opportunities WHERE crm_configuration_id = 2\n# order by id desc;\n# and crm_provider_id = '49908861993';\n\n\nselect * from activity_providers where id IN (443, 202, 203, 227);\n\nselect * from activity_imports where id = 795889;\n\nselect c.id, c.provider, c.settings, t.* from teams t join crm_configurations c on t.id = c.team_id\nwhere c.provider = 'hubspot';\n\nselect * from crm_configurations crm JOIN teams t on crm.team_id = t.id\nwhere provider = 'hubspot';\nSELECT * FROM teams WHERE id = 31;\nSELECT * FROM users WHERE id = 257;\nSELECT * FROM opportunities WHERE team_id = 2;\n\nselect * from opportunity_contacts where opportunity_id = 5124;\nselect * from contacts where id IN (3850,3853,3851,4073,4140,4155,4480,4530,4623,5986,513,687,1806,1523,3613)\n\nselect * from activities where crm_configuration_id = 13;\n\nSELECT * FROM activities WHERE uuid_to_bin('826619ce-ec8e-4e59-8467-a01f5f6ad71e') = uuid; # 418141\n\n\nselect id, team_id, crm_provider_id from crm_configurations where provider = 'hubspot' and crm_provider_id IS NOT NULL;\nSELECT * FROM accounts WHERE team_id = 2 and crm_provider_id = '1212213464' order by id desc;\nSELECT * FROM contacts WHERE team_id = 2 and account_id = 5189 order by id desc;\nSELECT * FROM contacts WHERE team_id = 2 order by id desc;\nselect * from opportunity_contacts where contact_id = 6223;\nSELECT * FROM opportunities WHERE team_id = 2 and account_id = 5189 order by id desc;\n\nselect * from crm_profiles where crm_configuration_id = 2;\n\nselect * from activities where account_id = 46;","depth":4,"on_screen":true,"value":"# **************************** HS **************************************\n\nselect * from teams where id = 2; # 2\nselect * from features; # 2\nselect * from team_features where team_id = 2; # 2\nselect * from crm_configurations where id = 2; # 2\nselect * from users where team_id = 2; #\nselect * from playbooks where team_id = 2; # event 38\nselect * from playbook_categories where playbook_id = 38; #\n\nSELECT * FROM activities WHERE crm_configuration_id = 2 and crm_provider_id is not null order by id desc;\nhttps://app.hubspot.com/contacts/4392066/deal/16964514951/?engagement=96069102624\n https://app.staging.jiminny.com/playback/d5df34dc-bd66-4ff5-a7b3-8d3be30322a0\n\nSELECT * FROM activities WHERE uuid_to_bin('04fdcd0d-818f-4c53-92dc-6f18bc753ffd') = uuid;\n# 609126 softphone tr. 11241\n\nSELECT * FROM activities WHERE uuid_to_bin('6521bfcd-5a30-46e5-9f74-5440fd48befd') = uuid;\n# 608874 conference tr. 11226 crmId: 103422236596\n\nselect * from ai_prompts where transcription_id IN (11241, 11226);\nselect * from activity_summary_logs where activity_id = 608874;\n\nselect * from sidekick_settings;\nselect * from default_activity_types;\n\nselect * from crm_field_data where activity_id = 1223;\n\nselect * from crm_layouts where crm_configuration_id = 2;\nSELECT * FROM crm_layout_entities WHERE crm_layout_id IN (554);\nselect * from crm_fields where crm_configuration_id = 11 and object_type = 'event';\nSELECT * FROM crm_field_values WHERE crm_field_id IN (1455,1450);\n\nSELECT * FROM crm_field_data WHERE crm_layout_entity_id = 971;\nSELECT * FROM crm_field_data WHERE crm_layout_entity_id IN (6494,6495,6496,6497,6498,6499);\n\nSELECT\n CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,\n u.email,\n sa.*,\n t.owner_id FROM social_accounts sa\nJOIN users u\n on u.id = sa.sociable_id\nJOIN teams t on t.id = u.team_id\nWHERE u.team_id = 2 and sa.provider = 'hubspot';\n\nselect * from social_accounts where id = 1499;\n\nselect * from opportunities where team_id = 2\nand crm_provider_id IN ('51317301383');\n\nselect * from contacts where id = 85;\n\nselect * from opportunities where team_id = 2 order by id desc;\nselect * from opportunities where team_id = 2 and crm_provider_id = '51317301383'; # 5112\nselect * from opportunities where team_id = 2 and crm_provider_id = '55976759904'; # 5112\nselect * from opportunity_contacts where opportunity_id = 5117;\nselect * from crm_field_data where object_id = 1365;\nSELECT * FROM crm_fields WHERE id IN (1405, 1407, 1972, 2128);\n\nselect * from features;\nselect * from team_features where team_id IN (1);\nselect * from team_features where feature_id IN (36);\n\nSHOW CREATE TABLE opportunity_contacts;\nSELECT * FROM opportunity_contacts WHERE crm_provider_id = '111751';\n\n# $slug = 'HUBSPOT_WEBHOOK_SYNC';\n# $team = Jiminny\\Models\\Team::find(2);\n# $feature = Feature::query()->where('slug', $slug)->first();\n# TeamFeature::query()->create(['feature_id' => $feature->getId(),'team_id' => $team->getId()]);\n\n# hubspot_webhook_metrics\n\nselect * from opportunities where team_id = 2 and crm_provider_id IN ('374720564','14527423589','49908861993','50435771779'); # 1365\nSELECT * FROM opportunity_contacts WHERE opportunity_id = '414';\nSELECT * FROM opportunity_contacts WHERE crm_provider_id = '131501';\nselect * from contacts where id in (414, 464);\n\nselect * from activities where crm_configuration_id = 2;\n\nselect settings from crm_configurations where id = 11;\n\nselect * from teams; # 1, 2\nselect * from users;\nselect * from crm_configurations where id = 39;\nselect * from team_features where team_id = 2;\nselect * from features;\n# SELECT * FROM opportunities WHERE crm_configuration_id = 2\n# order by id desc;\n# and crm_provider_id = '49908861993';\n\n\nselect * from activity_providers where id IN (443, 202, 203, 227);\n\nselect * from activity_imports where id = 795889;\n\nselect c.id, c.provider, c.settings, t.* from teams t join crm_configurations c on t.id = c.team_id\nwhere c.provider = 'hubspot';\n\nselect * from crm_configurations crm JOIN teams t on crm.team_id = t.id\nwhere provider = 'hubspot';\nSELECT * FROM teams WHERE id = 31;\nSELECT * FROM users WHERE id = 257;\nSELECT * FROM opportunities WHERE team_id = 2;\n\nselect * from opportunity_contacts where opportunity_id = 5124;\nselect * from contacts where id IN (3850,3853,3851,4073,4140,4155,4480,4530,4623,5986,513,687,1806,1523,3613)\n\nselect * from activities where crm_configuration_id = 13;\n\nSELECT * FROM activities WHERE uuid_to_bin('826619ce-ec8e-4e59-8467-a01f5f6ad71e') = uuid; # 418141\n\n\nselect id, team_id, crm_provider_id from crm_configurations where provider = 'hubspot' and crm_provider_id IS NOT NULL;\nSELECT * FROM accounts WHERE team_id = 2 and crm_provider_id = '1212213464' order by id desc;\nSELECT * FROM contacts WHERE team_id = 2 and account_id = 5189 order by id desc;\nSELECT * FROM contacts WHERE team_id = 2 order by id desc;\nselect * from opportunity_contacts where contact_id = 6223;\nSELECT * FROM opportunities WHERE team_id = 2 and account_id = 5189 order by id desc;\n\nselect * from crm_profiles where crm_configuration_id = 2;\n\nselect * from activities where account_id = 46;","role_description":"text entry area","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Sync Changes","depth":4,"bounds":{"left":0.27027926,"top":1.0,"width":0.008643617,"height":0.0},"on_screen":false,"role_description":"button","is_enabled":false,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Hide This Notification","depth":4,"bounds":{"left":0.27027926,"top":1.0,"width":0.008643617,"height":0.0},"on_screen":false,"role_description":"button","is_enabled":false,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Code 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Error","depth":4,"bounds":{"left":0.27027926,"top":1.0,"width":0.00731383,"height":0.0},"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Next Highlighted Error","depth":4,"bounds":{"left":0.27027926,"top":1.0,"width":0.006981383,"height":0.0},"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXTextArea","text":"<?php\n\ndeclare(strict_types=1);\n\nnamespace Jiminny\\Services\\Crm\\Hubspot;\n\nuse HubSpot\\Client\\Crm\\Deals\\ApiException as DealApiException;\nuse HubSpot\\Client\\Crm\\Contacts\\ApiException as ContactApiException;\nuse HubSpot\\Client\\Crm\\Companies\\ApiException as CompanyApiException;\nuse HubSpot\\Client\\Crm\\Contacts\\Model\\SimplePublicObjectWithAssociations as ContactsWithAssociations;\nuse HubSpot\\Client\\Crm\\Companies\\Model\\SimplePublicObjectWithAssociations as CompaniesWithAssociations;\nuse HubSpot\\Client\\Crm\\Deals\\Model\\SimplePublicObjectWithAssociations as DealWithAssociations;\nuse HubSpot\\Client\\Crm\\Objects\\Model\\SimplePublicObjectInput;\nuse HubSpot\\Client\\Crm\\Objects\\Model\\SimplePublicObjectWithAssociations as ObjectWithAssociations;\nuse HubSpot\\Client\\Crm\\Pipelines\\Model\\Error;\nuse HubSpot\\Client\\Crm\\Pipelines\\Model\\PipelineStage;\nuse HubSpot\\Client\\Crm\\Properties\\Model\\Property;\nuse HubSpot\\Discovery\\Discovery;\nuse Jiminny\\Component\\Utility\\Service\\ProviderRateLimiter;\nuse Jiminny\\Exceptions\\CrmException;\nuse Jiminny\\Exceptions\\RateLimitException;\nuse Jiminny\\Exceptions\\SocialAccountTokenInvalidException;\nuse Jiminny\\Jobs\\Crm\\NoteObject;\nuse Jiminny\\Models\\Crm\\Field;\nuse Jiminny\\Services\\Crm\\BaseClient;\nuse Jiminny\\Services\\Crm\\Hubspot\\DTO\\Response\\Owner;\nuse Jiminny\\Services\\SocialAccountService;\nuse SevenShores\\Hubspot\\Exceptions\\BadRequest;\nuse SevenShores\\Hubspot\\Exceptions\\HubspotException;\nuse SevenShores\\Hubspot\\Factory;\nuse SevenShores\\Hubspot\\Http\\Response;\nuse Jiminny\\Services\\Crm\\Hubspot\\Pagination\\HubspotPaginationService;\nuse Throwable;\n\n/**\n * @phpstan-type CrmFieldOption array{id:string, label:string, value?:string}\n */\nclass Client extends BaseClient implements HubspotClientInterface\n{\n public const string MIN_API_VERSION = '2';\n\n public const string BASE_URL = 'https://api.hubapi.com';\n\n public const int ASSOCIATIONS_BATCH_SIZE_LIMIT = 1000;\n\n private HubspotPaginationService $paginationService;\n private HubspotTokenManager $tokenManager;\n private ProviderRateLimiter $rateLimiter;\n\n public function __construct(\n SocialAccountService $socialAccountService,\n HubspotPaginationService $paginationService,\n HubspotTokenManager $tokenManager,\n ProviderRateLimiter $rateLimiter,\n ) {\n parent::__construct($socialAccountService);\n $this->paginationService = $paginationService;\n $this->tokenManager = $tokenManager;\n $this->rateLimiter = $rateLimiter;\n\n $this->setBaseUrl(self::BASE_URL);\n $this->setVersion(self::MIN_API_VERSION);\n }\n\n /**\n * Single entry point for every HubSpot API call. Enforces the per-portal\n * rate limit configured in the rate_limits table (morphed to the current\n * Configuration) and reacts to a real 429 from HubSpot by translating it\n * into a RateLimitException carrying Retry-After.\n *\n * Wrap any outbound HubSpot call (SDK or raw HTTP) like:\n *\n * $this->executeRequest(fn () => $this->getNewInstance()->crm()->...);\n *\n * @template T\n * @param callable(): T $apiCall\n * @return T\n *\n * @throws RateLimitException\n */\n private function executeRequest(callable $apiCall)\n {\n if (! $this->rateLimiter->canMakeRequest($this->config)) {\n $retryAfter = $this->rateLimiter->requestAvailableIn($this->config);\n\n $this->log->warning('[Hubspot] Rate limit exceeded, deferring request', [\n 'team_id' => $this->config->team_id,\n 'config_id' => $this->config->getId(),\n 'retry_after' => $retryAfter,\n ]);\n\n throw new RateLimitException(\n 'Hubspot rate limit reached for configuration ' . $this->config->getId(),\n $retryAfter,\n );\n }\n\n $this->rateLimiter->incrementRequestCount($this->config);\n\n try {\n return $apiCall();\n } catch (Throwable $e) {\n if ($this->isHubspotRateLimit($e)) {\n $retryAfter = $this->parseRetryAfter($e);\n\n $this->log->warning('[Hubspot] Received 429 from API', [\n 'team_id' => $this->config->team_id,\n 'config_id' => $this->config->getId(),\n 'retry_after' => $retryAfter,\n 'reason' => $e->getMessage(),\n ]);\n\n throw new RateLimitException('Hubspot returned 429', $retryAfter, $e);\n }\n\n throw $e;\n }\n }\n\n private function isHubspotRateLimit(Throwable $e): bool\n {\n return method_exists($e, 'getCode') && (int) $e->getCode() === 429;\n }\n\n private function parseRetryAfter(Throwable $e): int\n {\n if (method_exists($e, 'getResponseHeaders')) {\n $headers = $e->getResponseHeaders() ?: [];\n $value = $headers['Retry-After'] ?? $headers['retry-after'] ?? null;\n if (is_array($value)) {\n $value = $value[0] ?? null;\n }\n if (is_numeric($value)) {\n return (int) $value;\n }\n }\n\n return 10;\n }\n\n public function getMinimumApiVersion(): string\n {\n return self::MIN_API_VERSION;\n }\n\n public function getInstance(): Factory\n {\n return new Factory([\n 'key' => $this->accessToken,\n 'oauth2' => true,\n 'base_url' => $this->baseUrl,\n ]);\n }\n\n public function getNewInstance(): Discovery\n {\n return \\HubSpot\\Factory::createWithAccessToken($this->accessToken);\n }\n\n /**\n * Secondly and daily limits for Hubspot API\n *\n * Product Tier: Free & Starter | Professional & Enterprise | API add-on (any tier)\n * Burst: 100/10 seconds | 150/10 seconds | 200/10 seconds\n * Daily: 250,000 | 500,000 | 1,000,000\n *\n * Official documentation states: The search endpoints are rate limited to five requests per second.\n * Since with 5 RPS were still hitting secondly rate limits we lowered it to 4\n */\n public function getPaginatedData(array $payload, string $type, int $offset = 0): array\n {\n $total = 0;\n $lastId = null;\n $rows = [];\n foreach ($this->getPaginatedDataGenerator($payload, $type, $offset, $total, $lastId) as $row) {\n $rows[] = $row;\n }\n\n return ['results' => $rows, 'total' => $total, 'last_record' => $lastId];\n }\n\n /**\n * @throws HubspotException\n * @throws SocialAccountTokenInvalidException\n * @throws BadRequest\n */\n public function getPaginatedDataGenerator(\n array $payload,\n string $type,\n int $offset = 0,\n int &$total = 0,\n ?string &$lastRecordId = null\n ): \\Generator {\n return $this->paginationService->getPaginatedDataGenerator(\n $this,\n $payload,\n $type,\n $offset,\n $total,\n $lastRecordId\n );\n }\n\n /**\n * @throws DealApiException\n * @throws CrmException\n */\n public function getOpportunityById(string $crmId, array $fields): array\n {\n try {\n $deal = $this->executeRequest(fn () => $this->getNewInstance()->crm()->deals()->basicApi()->getById(\n $crmId,\n implode(',', $fields),\n 'companies,contacts'\n ));\n } catch (DealApiException $e) {\n $this->log->info('[Hubspot] Failed to fetch opportunity', [\n 'crm_id' => $crmId,\n 'reason' => $e->getMessage(),\n ]);\n\n throw $e;\n }\n\n if (! $deal instanceof DealWithAssociations) {\n throw new CrmException('Deal not found');\n }\n\n return [\n 'id' => $deal->getId(),\n 'properties' => $deal->getProperties(),\n 'associations' => $deal->getAssociations(),\n ];\n }\n\n /**\n * Generic batch read method for HubSpot objects\n *\n * @param string $objectType The object type ('deals', 'companies', 'contacts')\n * @param array<string> $crmIds Array of HubSpot object IDs (max 100)\n * @param array<string> $fields Array of property names to fetch\n *\n * @return array<string, array> Array keyed by CRM ID with object data\n */\n private function batchReadObjects(string $objectType, array $crmIds, array $fields): array\n {\n if (empty($crmIds)) {\n return [];\n }\n\n $this->validateBatchSize($objectType, $crmIds);\n $this->ensureValidToken();\n\n try {\n $batchConfig = $this->createBatchConfiguration($objectType);\n $batchReadRequest = $this->prepareBatchRequest($batchConfig, $crmIds, $fields);\n $response = $batchConfig['api']->read($batchReadRequest);\n\n $this->validateApiResponse($response, $objectType);\n\n $results = $this->processApiResults($response);\n $this->logBatchResults($objectType, $crmIds, $results);\n\n return $results;\n } catch (\\Throwable $e) {\n $this->handleBatchError($e, $objectType, $crmIds);\n }\n }\n\n private function validateBatchSize(string $objectType, array $crmIds): void\n {\n if (count($crmIds) > 100) {\n throw new \\InvalidArgumentException(\"Batch size cannot exceed 100 {$objectType}\");\n }\n }\n\n private function createBatchConfiguration(string $objectType): array\n {\n $configurations = [\n 'deals' => [\n 'batchReadRequest' => new \\HubSpot\\Client\\Crm\\Deals\\Model\\BatchReadInputSimplePublicObjectId(),\n 'inputClass' => \\HubSpot\\Client\\Crm\\Deals\\Model\\SimplePublicObjectId::class,\n 'api' => $this->getNewInstance()->crm()->deals()->batchApi(),\n ],\n 'companies' => [\n 'batchReadRequest' => new \\HubSpot\\Client\\Crm\\Companies\\Model\\BatchReadInputSimplePublicObjectId(),\n 'inputClass' => \\HubSpot\\Client\\Crm\\Companies\\Model\\SimplePublicObjectId::class,\n 'api' => $this->getNewInstance()->crm()->companies()->batchApi(),\n ],\n 'contacts' => [\n 'batchReadRequest' => new \\HubSpot\\Client\\Crm\\Contacts\\Model\\BatchReadInputSimplePublicObjectId(),\n 'inputClass' => \\HubSpot\\Client\\Crm\\Contacts\\Model\\SimplePublicObjectId::class,\n 'api' => $this->getNewInstance()->crm()->contacts()->batchApi(),\n ],\n ];\n\n if (! isset($configurations[$objectType])) {\n throw new \\InvalidArgumentException(\"Unsupported object type: {$objectType}\");\n }\n\n return $configurations[$objectType];\n }\n\n private function prepareBatchRequest(array $batchConfig, array $crmIds, array $fields): object\n {\n $batchReadRequest = $batchConfig['batchReadRequest'];\n $inputClass = $batchConfig['inputClass'];\n\n $inputs = array_map(function ($crmId) use ($inputClass) {\n $input = new $inputClass();\n $input->setId($crmId);\n\n return $input;\n }, $crmIds);\n\n $batchReadRequest->setInputs($inputs);\n $batchReadRequest->setProperties($fields);\n\n return $batchReadRequest;\n }\n\n private function validateApiResponse($response, string $objectType): void\n {\n if (! $response) {\n throw new CrmException(\"HubSpot API returned null response for {$objectType} batch read\");\n }\n }\n\n private function processApiResults($response): array\n {\n $results = [];\n $responseResults = $response->getResults();\n\n if ($responseResults) {\n foreach ($responseResults as $object) {\n if ($object && $object->getId()) {\n $results[$object->getId()] = [\n 'id' => $object->getId(),\n 'properties' => $object->getProperties() ?: [],\n ];\n }\n }\n }\n\n return $results;\n }\n\n private function logBatchResults(string $objectType, array $crmIds, array $results): void\n {\n $this->log->info(\"[HubSpot] Batch fetched {$objectType}\", [\n 'requested_count' => count($crmIds),\n 'returned_count' => count($results),\n 'crm_ids' => $crmIds,\n ]);\n }\n\n private function handleBatchError(\\Throwable $e, string $objectType, array $crmIds): void\n {\n $errorMessage = $e->getMessage() ?: 'Unknown error';\n $errorTrace = $e->getTraceAsString() ?: 'No trace available';\n\n $this->log->error(\"[HubSpot] Failed to batch fetch {$objectType}\", [\n 'crm_ids' => $crmIds,\n 'error' => $errorMessage,\n 'trace' => $errorTrace,\n ]);\n\n throw new CrmException(\"Failed to batch fetch {$objectType}: \" . $errorMessage);\n }\n\n /**\n * Batch read multiple opportunities by their CRM IDs\n *\n * @param array<string> $crmIds Array of HubSpot deal IDs (max 100)\n * @param array<string> $fields Array of property names to fetch\n *\n * @return array<string, array> Array keyed by CRM ID with opportunity data\n */\n public function getOpportunitiesByIds(array $crmIds, array $fields): array\n {\n return $this->batchReadObjects('deals', $crmIds, $fields);\n }\n\n /**\n * Batch read multiple companies by their CRM IDs\n *\n * @param array<string> $crmIds Array of HubSpot company IDs (max 100)\n * @param array<string> $fields Array of property names to fetch\n *\n * @return array<string, array> Array keyed by CRM ID with company data\n */\n public function getCompaniesByIds(array $crmIds, array $fields): array\n {\n return $this->batchReadObjects('companies', $crmIds, $fields);\n }\n\n /**\n * Batch read multiple contacts by their CRM IDs\n *\n * @param array<string> $crmIds Array of HubSpot contact IDs (max 100)\n * @param array<string> $fields Array of property names to fetch\n *\n * @return array<string, array> Array keyed by CRM ID with contact data\n */\n public function getContactsByIds(array $crmIds, array $fields): array\n {\n return $this->batchReadObjects('contacts', $crmIds, $fields);\n }\n\n /**\n * @throws CompanyApiException\n * @throws CrmException\n */\n public function getAccountById(string $crmId, array $fields): array\n {\n try {\n $company = $this->getNewInstance()->crm()->companies()->basicApi()->getById(\n $crmId,\n implode(',', $fields),\n );\n } catch (CompanyApiException $e) {\n $this->log->info('[Hubspot] Failed to fetch account', [\n 'crm_id' => $crmId,\n 'reason' => $e->getMessage(),\n ]);\n\n throw $e;\n }\n\n if (! $company instanceof CompaniesWithAssociations) {\n throw new CrmException('Account not found');\n }\n\n return [\n 'id' => $company->getId(),\n 'properties' => $company->getProperties(),\n ];\n }\n\n /**\n * @throws ContactApiException\n * @throws CrmException\n */\n public function getContactById(string $crmId, array $fields): array\n {\n try {\n $contact = $this->getNewInstance()->crm()->contacts()->basicApi()->getById(\n $crmId,\n implode(',', $fields)\n );\n } catch (ContactApiException $e) {\n $this->log->info('[Hubspot] Failed to fetch contact', [\n 'crm_id' => $crmId,\n 'reason' => $e->getMessage(),\n ]);\n\n throw $e;\n }\n\n if (! $contact instanceof ContactsWithAssociations) {\n throw new CrmException('Contact not found');\n }\n\n return [\n 'id' => $contact->getId(),\n 'properties' => $contact->getProperties(),\n ];\n }\n\n /**\n * This is email search request that Hubspot offers as GET (more generous quota)\n */\n public function getContactByEmail(string $email, array $fields = []): array\n {\n try {\n $contact = $this->getNewInstance()->crm()->contacts()->basicApi()->getById(\n $email,\n implode(',', $fields),\n null,\n false,\n 'email'\n );\n\n return [\n 'id' => $contact->getId(),\n 'properties' => $contact->getProperties(),\n ];\n } catch (ContactApiException $e) {\n $this->log->info('[Hubspot] Failed to fetch contact', [\n 'email' => $email,\n 'reason' => $e->getMessage(),\n ]);\n\n return [];\n }\n }\n\n /**\n * @throws CrmException\n */\n public function fetchProperty(string $objectType, string $propertyId): Property\n {\n $result = $this->getNewInstance()->crm()->properties()->coreApi()->getByName($objectType, $propertyId);\n\n if (! $result instanceof Property) {\n $this->log->error('[Hubspot] Failed to fetch property', [\n 'object_type' => $objectType,\n 'property_id' => $propertyId,\n 'reason' => $result->getMessage(),\n ]);\n\n throw new CrmException('Failed to fetch property');\n }\n\n return $result;\n }\n\n /**\n * @return array<CrmFieldOption>\n */\n public function fetchPropertyOptions(string $objectType, string $propertyId): array\n {\n /** @var array<CrmFieldOption> */\n return $this->fetchProperty($objectType, $propertyId)->getOptions();\n }\n\n /**\n * @return array<array{id:string, label:string, deleted:bool}>\n */\n public function fetchCallDispositions(): array\n {\n /** @var Response $response */\n $response = $this->getInstance()->engagements()->getCallDispositions();\n\n /**\n * @var array<array{\n * id:string,\n * label:string,\n * deleted: bool\n * }>\n */\n return $response->toArray();\n }\n\n /**\n * @return array<CrmFieldOption>\n */\n public function fetchOpportunityPipelineStages(): array\n {\n $stages = [];\n $apiResponse = $this->getNewInstance()->crm()->pipelines()->pipelinesApi()->getAll('deals');\n\n if ($apiResponse instanceof Error) {\n $this->log->error('[Hubspot] Failed to fetch opportunity pipelines', [\n 'reason' => $apiResponse->getMessage(),\n ]);\n\n return [];\n }\n\n foreach ($apiResponse->getResults() as $pipeline) {\n $pipelineStages = array_map(\n static function (PipelineStage $stage) {\n return [\n 'id' => $stage->getId(),\n 'label' => $stage->getLabel(),\n ];\n },\n $pipeline->getStages()\n );\n\n $stages = array_merge($stages, $pipelineStages);\n }\n\n return $stages;\n }\n\n public function fetchOpportunityPipelines(): array\n {\n $pipelines = [];\n\n try {\n $apiResponse = $this->makeRequest('/crm/v3/pipelines/deals');\n } catch (\\Exception $e) {\n $this->log->info('[Hubspot] Failed to fetch opportunity pipelines', [\n 'reason' => $e->getMessage(),\n ]);\n\n return [];\n }\n\n $response = $apiResponse->toArray();\n\n foreach ($response['results'] as $pipeline) {\n $pipelines[] = [\n 'id' => $pipeline['id'],\n 'label' => $pipeline['label'],\n ];\n }\n\n return $pipelines;\n }\n\n /**\n * @return array<CrmFieldOption>\n */\n public function fetchMeetingOutcomeFieldOptions(Field $field): array\n {\n return $field->getCrmProviderId() === 'meetingOutcome'\n ? $this->fetchMeetingOutcomeTypes()\n : $this->fetchCallActivityTypes();\n }\n\n public function fetchMeetingOutcomeTypes(): array\n {\n return $this->extractMeetingTypeOptions(\n 'https://api.hubapi.com/crm/v3/properties/meeting/hs_meeting_outcome'\n );\n }\n\n public function fetchCallActivityTypes(): array\n {\n return $this->extractMeetingTypeOptions(\n 'https://api.hubapi.com/crm/v3/properties/call/hs_activity_type'\n );\n }\n\n private function extractMeetingTypeOptions(string $endpoint): array\n {\n /** @var Response $response */\n $response = $this->getInstance()\n ->getClient()\n ->request('GET', $endpoint);\n\n /**\n * @var array<array{\n * value: string,\n * label: string,\n * displayOrder: int\n * }> $optionData\n */\n $optionData = $response->toArray()['options'] ?? [];\n\n $options = [];\n foreach ($optionData as $item) {\n $options[] = [\n 'id' => $item['value'],\n 'value' => $item['value'],\n 'label' => $item['label'],\n 'display_order' => $item['displayOrder'],\n ];\n }\n\n return $options;\n }\n\n /**\n * @return array<CrmFieldOption>\n */\n public function fetchDispositionFieldOptions(): array\n {\n $options = [];\n\n $dispositions = $this->fetchCallDispositions();\n\n foreach ($dispositions as $disposition) {\n if ($disposition['deleted'] !== false) {\n continue;\n }\n\n $option['value'] = $disposition['id'];\n $option['id'] = $disposition['id'];\n $option['label'] = $disposition['label'];\n\n $options[] = $option;\n }\n\n return $options;\n }\n\n /**\n * @return array<CrmFieldOption>\n */\n public function fetchOpportunityFieldOptions(Field $field): array\n {\n if ($field->isStageField()) {\n return $this->fetchOpportunityPipelineStages();\n }\n\n if ($field->isPipelineField()) {\n return $this->fetchOpportunityPipelines();\n }\n\n return $this->fetchPropertyOptions('deals', $field->getCrmProviderId());\n }\n\n /**\n * @throws BadRequest\n * @throws HubspotException\n */\n public function makeRequest(string $endpoint, $method = 'GET', $payload = [], ?string $queryString = null)\n {\n $endpoint = self::BASE_URL . $endpoint;\n\n if ($method === 'GET') {\n return $this->getInstance()->getClient()?->request(\n method: $method,\n endpoint: $endpoint,\n query_string: $queryString\n );\n } else {\n return $this->getInstance()->getClient()->request($method, $endpoint, [\n 'json' => ($payload),\n ]);\n }\n }\n\n /**\n * @throws BadRequest\n * @throws HubspotException\n */\n public function createMeeting(array $payload): Response\n {\n $endpoint = '/crm/v3/objects/meetings';\n\n return $this->makeRequest($endpoint, 'POST', $payload);\n }\n\n /**\n * @throws BadRequest\n * @throws HubspotException\n */\n public function updateMeeting(string $meetingId, array $payload): Response\n {\n $endpoint = '/crm/v3/objects/meetings/' . $meetingId;\n\n return $this->makeRequest($endpoint, 'PATCH', $payload);\n }\n\n /**\n * @throws \\Exception\n */\n public function createNote(\n string $body,\n string $ownerId,\n int $timestamp,\n string $objectId,\n NoteObject $noteObject\n ): ?string {\n try {\n $noteInput = new SimplePublicObjectInput([\n 'properties' => [\n 'hs_note_body' => $body,\n 'hubspot_owner_id' => $ownerId,\n 'hs_timestamp' => $timestamp,\n ],\n ]);\n\n // Create note\n $note = $this->getNewInstance()->crm()->objects()->basicApi()->create('note', $noteInput);\n\n $this->getNewInstance()->crm()->objects()->associationsApi()->create(\n 'note',\n $note->getId(),\n $this->getNoteObject($noteObject),\n $objectId,\n $this->getNoteAssociationType($noteObject),\n );\n\n return $note->getId();\n } catch (\\Exception $e) {\n $this->log->error('[Hubspot] Failed to create note', [\n 'objectId' => $objectId,\n 'noteObject' => $noteObject->getObjectType(),\n 'reason' => $e->getMessage(),\n ]);\n\n \\Sentry::captureException($e);\n }\n\n return null;\n }\n\n public function updateEngagement(string $objectId, array $engagement, array $metadata): void\n {\n $this->getInstance()->engagements()->update($objectId, $engagement, $metadata);\n }\n\n public function getEngagementData(string $engagementId): array\n {\n $engagement = $this->getInstance()->engagements()->get($engagementId);\n\n return $engagement->toArray();\n }\n\n public function createEngagement(array $engagement, array $associations, array $metadata): Response\n {\n return $this->getInstance()\n ->engagements()\n ->create($engagement, $associations, $metadata);\n }\n\n public function isUnauthorizedException(\\Exception $e): bool\n {\n // Check for specific HubSpot API exception types first\n if ($e instanceof BadRequest) {\n // BadRequest can contain 401 status codes\n return $e->getCode() === 401;\n }\n\n // Check for HTTP client exceptions with status codes\n if ($e instanceof \\GuzzleHttp\\Exception\\RequestException && $e->hasResponse()) {\n $response = $e->getResponse();\n if ($response !== null) {\n return $response->getStatusCode() === 401;\n }\n }\n\n // Check for Guzzle HTTP exceptions\n if ($e instanceof \\GuzzleHttp\\Exception\\ClientException) {\n return $e->getCode() === 401;\n }\n\n // Fallback to string matching as last resort, but be more specific\n $message = strtolower($e->getMessage());\n\n return str_contains($message, '401 unauthorized') ||\n str_contains($message, 'http 401') ||\n str_contains($message, 'status code 401') ||\n (preg_match('/\\b401\\b/', $message) && str_contains($message, 'unauthorized'));\n }\n\n /**\n * Validates and refreshes the access token if needed before API requests.\n * This ensures long-running processes don't fail due to token expiration.\n *\n * @throws SocialAccountTokenInvalidException\n */\n public function ensureValidToken(): void\n {\n if ($this->oauthAccount === null) {\n return;\n }\n\n $newToken = $this->tokenManager->ensureValidToken($this->oauthAccount);\n if ($newToken !== null) {\n $this->accessToken = $newToken;\n }\n }\n\n public function getConfig()\n {\n return $this->config;\n }\n\n // returns only active (archived=false)\n public function getOwners(): array\n {\n return $this->getNewInstance()->crm()->owners()->getAll();\n }\n\n /**\n * @param bool $archived\n *\n * @return array<Owner>|[]\n */\n public function getOwnersArchived(bool $archived = true): array\n {\n $endpoint = '/crm/v3/owners';\n $queryParams = [\n 'archived' => $archived ? 'true' : 'false',\n ];\n $queryString = http_build_query($queryParams);\n\n $owners = [];\n\n try {\n $response = $this->makeRequest(endpoint: $endpoint, queryString: $queryString);\n $responseData = $response?->toArray();\n\n foreach ($responseData['results'] as $result) {\n try {\n $owners[] = Owner::create($result);\n } catch (Throwable $e) {\n $this->log->error('[HubSpot] Failed to process owner data', [\n 'result' => $result,\n 'error' => $e->getMessage(),\n ]);\n\n continue;\n }\n }\n } catch (Throwable $e) {\n $this->log->error('HubSpot] Failed to fetch owners', [\n 'archived' => $archived,\n 'error' => $e->getMessage(),\n ]);\n\n return [];\n }\n\n return $owners;\n }\n\n public function getMeeting(string $engagementId): ObjectWithAssociations\n {\n return $this->getNewInstance()->crm()->objects()->basicApi()\n ->getById('meeting', $engagementId, null, 'contact,company,deal');\n }\n\n public function deleteEngagement(string $engagementId): void\n {\n $this->getInstance()->engagements()->delete((int) $engagementId);\n }\n\n public function getAssociationsData(array $ids, string $fromObject, string $toObject): array\n {\n $associationData = [];\n $idChunks = array_chunk($ids, self::ASSOCIATIONS_BATCH_SIZE_LIMIT);\n\n foreach ($idChunks as $idChunk) {\n try {\n $batchInput = new \\HubSpot\\Client\\Crm\\Associations\\Model\\BatchInputPublicObjectId();\n $batchInput->setInputs(array_map(function ($id) {\n $publicObjectId = new \\HubSpot\\Client\\Crm\\Associations\\Model\\PublicObjectId();\n $publicObjectId->setId($id);\n\n return $publicObjectId;\n }, $idChunk));\n\n $associatedObjectsData = $this\n ->getNewInstance()\n ->crm()\n ->associations()\n ->batchApi()\n ->read($fromObject, $toObject, $batchInput);\n\n if ($associatedObjectsData instanceof \\HubSpot\\Client\\Crm\\Associations\\Model\\BatchResponsePublicAssociationMulti) {\n foreach ($associatedObjectsData->getResults() as $association) {\n $from = $association->getFrom()->getId();\n $toAssociations = $association->getTo();\n\n if (! empty($toAssociations)) {\n $associationData[$from] = array_map(function ($item) {\n return $item->getId();\n }, $toAssociations);\n }\n }\n }\n } catch (\\Exception $e) {\n $this->log->error('[Hubspot] Failed to fetch associations', [\n 'from_object' => $fromObject,\n 'to_object' => $toObject,\n 'reason' => $e->getMessage(),\n ]);\n }\n }\n\n return $associationData;\n }\n\n /**\n * @throws \\Exception\n */\n private function getNoteAssociationType(NoteObject $noteObject): string\n {\n return match($noteObject) {\n NoteObject::Opportunity => 'note_to_deal',\n NoteObject::Lead, NoteObject::Contact => 'note_to_contact', // or 'note_to_lead' if your portal supports it\n NoteObject::Account => 'note_to_company',\n NoteObject::Call, NoteObject::Event => throw new \\Exception('Not supported'),\n };\n }\n\n /**\n * @throws \\Exception\n */\n private function getNoteObject(NoteObject $noteObject): string\n {\n return match($noteObject) {\n NoteObject::Opportunity => 'deal',\n NoteObject::Lead, NoteObject::Contact => 'contact',\n NoteObject::Account => 'company',\n NoteObject::Call, NoteObject::Event => throw new \\Exception('Not supported'),\n };\n }\n\n public function addAssociations(string $objectType, string $associationType, array $payload): Response\n {\n $endpoint = \"/crm/v4/associations/$objectType/$associationType/batch/create\";\n\n return $this->makeRequest($endpoint, 'POST', $payload);\n }\n\n public function removeAssociations(string $objectType, string $associationType, array $payload): Response\n {\n $endpoint = \"/crm/v4/associations/$objectType/$associationType/batch/archive\";\n\n return $this->makeRequest($endpoint, 'POST', $payload);\n }\n}","depth":4,"on_screen":true,"value":"<?php\n\ndeclare(strict_types=1);\n\nnamespace Jiminny\\Services\\Crm\\Hubspot;\n\nuse HubSpot\\Client\\Crm\\Deals\\ApiException as DealApiException;\nuse HubSpot\\Client\\Crm\\Contacts\\ApiException as ContactApiException;\nuse HubSpot\\Client\\Crm\\Companies\\ApiException as CompanyApiException;\nuse HubSpot\\Client\\Crm\\Contacts\\Model\\SimplePublicObjectWithAssociations as ContactsWithAssociations;\nuse HubSpot\\Client\\Crm\\Companies\\Model\\SimplePublicObjectWithAssociations as CompaniesWithAssociations;\nuse HubSpot\\Client\\Crm\\Deals\\Model\\SimplePublicObjectWithAssociations as DealWithAssociations;\nuse HubSpot\\Client\\Crm\\Objects\\Model\\SimplePublicObjectInput;\nuse HubSpot\\Client\\Crm\\Objects\\Model\\SimplePublicObjectWithAssociations as ObjectWithAssociations;\nuse HubSpot\\Client\\Crm\\Pipelines\\Model\\Error;\nuse HubSpot\\Client\\Crm\\Pipelines\\Model\\PipelineStage;\nuse HubSpot\\Client\\Crm\\Properties\\Model\\Property;\nuse HubSpot\\Discovery\\Discovery;\nuse Jiminny\\Component\\Utility\\Service\\ProviderRateLimiter;\nuse Jiminny\\Exceptions\\CrmException;\nuse Jiminny\\Exceptions\\RateLimitException;\nuse Jiminny\\Exceptions\\SocialAccountTokenInvalidException;\nuse Jiminny\\Jobs\\Crm\\NoteObject;\nuse Jiminny\\Models\\Crm\\Field;\nuse Jiminny\\Services\\Crm\\BaseClient;\nuse Jiminny\\Services\\Crm\\Hubspot\\DTO\\Response\\Owner;\nuse Jiminny\\Services\\SocialAccountService;\nuse SevenShores\\Hubspot\\Exceptions\\BadRequest;\nuse SevenShores\\Hubspot\\Exceptions\\HubspotException;\nuse SevenShores\\Hubspot\\Factory;\nuse SevenShores\\Hubspot\\Http\\Response;\nuse Jiminny\\Services\\Crm\\Hubspot\\Pagination\\HubspotPaginationService;\nuse Throwable;\n\n/**\n * @phpstan-type CrmFieldOption array{id:string, label:string, value?:string}\n */\nclass Client extends BaseClient implements HubspotClientInterface\n{\n public const string MIN_API_VERSION = '2';\n\n public const string BASE_URL = 'https://api.hubapi.com';\n\n public const int ASSOCIATIONS_BATCH_SIZE_LIMIT = 1000;\n\n private HubspotPaginationService $paginationService;\n private HubspotTokenManager $tokenManager;\n private ProviderRateLimiter $rateLimiter;\n\n public function __construct(\n SocialAccountService $socialAccountService,\n HubspotPaginationService $paginationService,\n HubspotTokenManager $tokenManager,\n ProviderRateLimiter $rateLimiter,\n ) {\n parent::__construct($socialAccountService);\n $this->paginationService = $paginationService;\n $this->tokenManager = $tokenManager;\n $this->rateLimiter = $rateLimiter;\n\n $this->setBaseUrl(self::BASE_URL);\n $this->setVersion(self::MIN_API_VERSION);\n }\n\n /**\n * Single entry point for every HubSpot API call. Enforces the per-portal\n * rate limit configured in the rate_limits table (morphed to the current\n * Configuration) and reacts to a real 429 from HubSpot by translating it\n * into a RateLimitException carrying Retry-After.\n *\n * Wrap any outbound HubSpot call (SDK or raw HTTP) like:\n *\n * $this->executeRequest(fn () => $this->getNewInstance()->crm()->...);\n *\n * @template T\n * @param callable(): T $apiCall\n * @return T\n *\n * @throws RateLimitException\n */\n private function executeRequest(callable $apiCall)\n {\n if (! $this->rateLimiter->canMakeRequest($this->config)) {\n $retryAfter = $this->rateLimiter->requestAvailableIn($this->config);\n\n $this->log->warning('[Hubspot] Rate limit exceeded, deferring request', [\n 'team_id' => $this->config->team_id,\n 'config_id' => $this->config->getId(),\n 'retry_after' => $retryAfter,\n ]);\n\n throw new RateLimitException(\n 'Hubspot rate limit reached for configuration ' . $this->config->getId(),\n $retryAfter,\n );\n }\n\n $this->rateLimiter->incrementRequestCount($this->config);\n\n try {\n return $apiCall();\n } catch (Throwable $e) {\n if ($this->isHubspotRateLimit($e)) {\n $retryAfter = $this->parseRetryAfter($e);\n\n $this->log->warning('[Hubspot] Received 429 from API', [\n 'team_id' => $this->config->team_id,\n 'config_id' => $this->config->getId(),\n 'retry_after' => $retryAfter,\n 'reason' => $e->getMessage(),\n ]);\n\n throw new RateLimitException('Hubspot returned 429', $retryAfter, $e);\n }\n\n throw $e;\n }\n }\n\n private function isHubspotRateLimit(Throwable $e): bool\n {\n return method_exists($e, 'getCode') && (int) $e->getCode() === 429;\n }\n\n private function parseRetryAfter(Throwable $e): int\n {\n if (method_exists($e, 'getResponseHeaders')) {\n $headers = $e->getResponseHeaders() ?: [];\n $value = $headers['Retry-After'] ?? $headers['retry-after'] ?? null;\n if (is_array($value)) {\n $value = $value[0] ?? null;\n }\n if (is_numeric($value)) {\n return (int) $value;\n }\n }\n\n return 10;\n }\n\n public function getMinimumApiVersion(): string\n {\n return self::MIN_API_VERSION;\n }\n\n public function getInstance(): Factory\n {\n return new Factory([\n 'key' => $this->accessToken,\n 'oauth2' => true,\n 'base_url' => $this->baseUrl,\n ]);\n }\n\n public function getNewInstance(): Discovery\n {\n return \\HubSpot\\Factory::createWithAccessToken($this->accessToken);\n }\n\n /**\n * Secondly and daily limits for Hubspot API\n *\n * Product Tier: Free & Starter | Professional & Enterprise | API add-on (any tier)\n * Burst: 100/10 seconds | 150/10 seconds | 200/10 seconds\n * Daily: 250,000 | 500,000 | 1,000,000\n *\n * Official documentation states: The search endpoints are rate limited to five requests per second.\n * Since with 5 RPS were still hitting secondly rate limits we lowered it to 4\n */\n public function getPaginatedData(array $payload, string $type, int $offset = 0): array\n {\n $total = 0;\n $lastId = null;\n $rows = [];\n foreach ($this->getPaginatedDataGenerator($payload, $type, $offset, $total, $lastId) as $row) {\n $rows[] = $row;\n }\n\n return ['results' => $rows, 'total' => $total, 'last_record' => $lastId];\n }\n\n /**\n * @throws HubspotException\n * @throws SocialAccountTokenInvalidException\n * @throws BadRequest\n */\n public function getPaginatedDataGenerator(\n array $payload,\n string $type,\n int $offset = 0,\n int &$total = 0,\n ?string &$lastRecordId = null\n ): \\Generator {\n return $this->paginationService->getPaginatedDataGenerator(\n $this,\n $payload,\n $type,\n $offset,\n $total,\n $lastRecordId\n );\n }\n\n /**\n * @throws DealApiException\n * @throws CrmException\n */\n public function getOpportunityById(string $crmId, array $fields): array\n {\n try {\n $deal = $this->executeRequest(fn () => $this->getNewInstance()->crm()->deals()->basicApi()->getById(\n $crmId,\n implode(',', $fields),\n 'companies,contacts'\n ));\n } catch (DealApiException $e) {\n $this->log->info('[Hubspot] Failed to fetch opportunity', [\n 'crm_id' => $crmId,\n 'reason' => $e->getMessage(),\n ]);\n\n throw $e;\n }\n\n if (! $deal instanceof DealWithAssociations) {\n throw new CrmException('Deal not found');\n }\n\n return [\n 'id' => $deal->getId(),\n 'properties' => $deal->getProperties(),\n 'associations' => $deal->getAssociations(),\n ];\n }\n\n /**\n * Generic batch read method for HubSpot objects\n *\n * @param string $objectType The object type ('deals', 'companies', 'contacts')\n * @param array<string> $crmIds Array of HubSpot object IDs (max 100)\n * @param array<string> $fields Array of property names to fetch\n *\n * @return array<string, array> Array keyed by CRM ID with object data\n */\n private function batchReadObjects(string $objectType, array $crmIds, array $fields): array\n {\n if (empty($crmIds)) {\n return [];\n }\n\n $this->validateBatchSize($objectType, $crmIds);\n $this->ensureValidToken();\n\n try {\n $batchConfig = $this->createBatchConfiguration($objectType);\n $batchReadRequest = $this->prepareBatchRequest($batchConfig, $crmIds, $fields);\n $response = $batchConfig['api']->read($batchReadRequest);\n\n $this->validateApiResponse($response, $objectType);\n\n $results = $this->processApiResults($response);\n $this->logBatchResults($objectType, $crmIds, $results);\n\n return $results;\n } catch (\\Throwable $e) {\n $this->handleBatchError($e, $objectType, $crmIds);\n }\n }\n\n private function validateBatchSize(string $objectType, array $crmIds): void\n {\n if (count($crmIds) > 100) {\n throw new \\InvalidArgumentException(\"Batch size cannot exceed 100 {$objectType}\");\n }\n }\n\n private function createBatchConfiguration(string $objectType): array\n {\n $configurations = [\n 'deals' => [\n 'batchReadRequest' => new \\HubSpot\\Client\\Crm\\Deals\\Model\\BatchReadInputSimplePublicObjectId(),\n 'inputClass' => \\HubSpot\\Client\\Crm\\Deals\\Model\\SimplePublicObjectId::class,\n 'api' => $this->getNewInstance()->crm()->deals()->batchApi(),\n ],\n 'companies' => [\n 'batchReadRequest' => new \\HubSpot\\Client\\Crm\\Companies\\Model\\BatchReadInputSimplePublicObjectId(),\n 'inputClass' => \\HubSpot\\Client\\Crm\\Companies\\Model\\SimplePublicObjectId::class,\n 'api' => $this->getNewInstance()->crm()->companies()->batchApi(),\n ],\n 'contacts' => [\n 'batchReadRequest' => new \\HubSpot\\Client\\Crm\\Contacts\\Model\\BatchReadInputSimplePublicObjectId(),\n 'inputClass' => \\HubSpot\\Client\\Crm\\Contacts\\Model\\SimplePublicObjectId::class,\n 'api' => $this->getNewInstance()->crm()->contacts()->batchApi(),\n ],\n ];\n\n if (! isset($configurations[$objectType])) {\n throw new \\InvalidArgumentException(\"Unsupported object type: {$objectType}\");\n }\n\n return $configurations[$objectType];\n }\n\n private function prepareBatchRequest(array $batchConfig, array $crmIds, array $fields): object\n {\n $batchReadRequest = $batchConfig['batchReadRequest'];\n $inputClass = $batchConfig['inputClass'];\n\n $inputs = array_map(function ($crmId) use ($inputClass) {\n $input = new $inputClass();\n $input->setId($crmId);\n\n return $input;\n }, $crmIds);\n\n $batchReadRequest->setInputs($inputs);\n $batchReadRequest->setProperties($fields);\n\n return $batchReadRequest;\n }\n\n private function validateApiResponse($response, string $objectType): void\n {\n if (! $response) {\n throw new CrmException(\"HubSpot API returned null response for {$objectType} batch read\");\n }\n }\n\n private function processApiResults($response): array\n {\n $results = [];\n $responseResults = $response->getResults();\n\n if ($responseResults) {\n foreach ($responseResults as $object) {\n if ($object && $object->getId()) {\n $results[$object->getId()] = [\n 'id' => $object->getId(),\n 'properties' => $object->getProperties() ?: [],\n ];\n }\n }\n }\n\n return $results;\n }\n\n private function logBatchResults(string $objectType, array $crmIds, array $results): void\n {\n $this->log->info(\"[HubSpot] Batch fetched {$objectType}\", [\n 'requested_count' => count($crmIds),\n 'returned_count' => count($results),\n 'crm_ids' => $crmIds,\n ]);\n }\n\n private function handleBatchError(\\Throwable $e, string $objectType, array $crmIds): void\n {\n $errorMessage = $e->getMessage() ?: 'Unknown error';\n $errorTrace = $e->getTraceAsString() ?: 'No trace available';\n\n $this->log->error(\"[HubSpot] Failed to batch fetch {$objectType}\", [\n 'crm_ids' => $crmIds,\n 'error' => $errorMessage,\n 'trace' => $errorTrace,\n ]);\n\n throw new CrmException(\"Failed to batch fetch {$objectType}: \" . $errorMessage);\n }\n\n /**\n * Batch read multiple opportunities by their CRM IDs\n *\n * @param array<string> $crmIds Array of HubSpot deal IDs (max 100)\n * @param array<string> $fields Array of property names to fetch\n *\n * @return array<string, array> Array keyed by CRM ID with opportunity data\n */\n public function getOpportunitiesByIds(array $crmIds, array $fields): array\n {\n return $this->batchReadObjects('deals', $crmIds, $fields);\n }\n\n /**\n * Batch read multiple companies by their CRM IDs\n *\n * @param array<string> $crmIds Array of HubSpot company IDs (max 100)\n * @param array<string> $fields Array of property names to fetch\n *\n * @return array<string, array> Array keyed by CRM ID with company data\n */\n public function getCompaniesByIds(array $crmIds, array $fields): array\n {\n return $this->batchReadObjects('companies', $crmIds, $fields);\n }\n\n /**\n * Batch read multiple contacts by their CRM IDs\n *\n * @param array<string> $crmIds Array of HubSpot contact IDs (max 100)\n * @param array<string> $fields Array of property names to fetch\n *\n * @return array<string, array> Array keyed by CRM ID with contact data\n */\n public function getContactsByIds(array $crmIds, array $fields): array\n {\n return $this->batchReadObjects('contacts', $crmIds, $fields);\n }\n\n /**\n * @throws CompanyApiException\n * @throws CrmException\n */\n public function getAccountById(string $crmId, array $fields): array\n {\n try {\n $company = $this->getNewInstance()->crm()->companies()->basicApi()->getById(\n $crmId,\n implode(',', $fields),\n );\n } catch (CompanyApiException $e) {\n $this->log->info('[Hubspot] Failed to fetch account', [\n 'crm_id' => $crmId,\n 'reason' => $e->getMessage(),\n ]);\n\n throw $e;\n }\n\n if (! $company instanceof CompaniesWithAssociations) {\n throw new CrmException('Account not found');\n }\n\n return [\n 'id' => $company->getId(),\n 'properties' => $company->getProperties(),\n ];\n }\n\n /**\n * @throws ContactApiException\n * @throws CrmException\n */\n public function getContactById(string $crmId, array $fields): array\n {\n try {\n $contact = $this->getNewInstance()->crm()->contacts()->basicApi()->getById(\n $crmId,\n implode(',', $fields)\n );\n } catch (ContactApiException $e) {\n $this->log->info('[Hubspot] Failed to fetch contact', [\n 'crm_id' => $crmId,\n 'reason' => $e->getMessage(),\n ]);\n\n throw $e;\n }\n\n if (! $contact instanceof ContactsWithAssociations) {\n throw new CrmException('Contact not found');\n }\n\n return [\n 'id' => $contact->getId(),\n 'properties' => $contact->getProperties(),\n ];\n }\n\n /**\n * This is email search request that Hubspot offers as GET (more generous quota)\n */\n public function getContactByEmail(string $email, array $fields = []): array\n {\n try {\n $contact = $this->getNewInstance()->crm()->contacts()->basicApi()->getById(\n $email,\n implode(',', $fields),\n null,\n false,\n 'email'\n );\n\n return [\n 'id' => $contact->getId(),\n 'properties' => $contact->getProperties(),\n ];\n } catch (ContactApiException $e) {\n $this->log->info('[Hubspot] Failed to fetch contact', [\n 'email' => $email,\n 'reason' => $e->getMessage(),\n ]);\n\n return [];\n }\n }\n\n /**\n * @throws CrmException\n */\n public function fetchProperty(string $objectType, string $propertyId): Property\n {\n $result = $this->getNewInstance()->crm()->properties()->coreApi()->getByName($objectType, $propertyId);\n\n if (! $result instanceof Property) {\n $this->log->error('[Hubspot] Failed to fetch property', [\n 'object_type' => $objectType,\n 'property_id' => $propertyId,\n 'reason' => $result->getMessage(),\n ]);\n\n throw new CrmException('Failed to fetch property');\n }\n\n return $result;\n }\n\n /**\n * @return array<CrmFieldOption>\n */\n public function fetchPropertyOptions(string $objectType, string $propertyId): array\n {\n /** @var array<CrmFieldOption> */\n return $this->fetchProperty($objectType, $propertyId)->getOptions();\n }\n\n /**\n * @return array<array{id:string, label:string, deleted:bool}>\n */\n public function fetchCallDispositions(): array\n {\n /** @var Response $response */\n $response = $this->getInstance()->engagements()->getCallDispositions();\n\n /**\n * @var array<array{\n * id:string,\n * label:string,\n * deleted: bool\n * }>\n */\n return $response->toArray();\n }\n\n /**\n * @return array<CrmFieldOption>\n */\n public function fetchOpportunityPipelineStages(): array\n {\n $stages = [];\n $apiResponse = $this->getNewInstance()->crm()->pipelines()->pipelinesApi()->getAll('deals');\n\n if ($apiResponse instanceof Error) {\n $this->log->error('[Hubspot] Failed to fetch opportunity pipelines', [\n 'reason' => $apiResponse->getMessage(),\n ]);\n\n return [];\n }\n\n foreach ($apiResponse->getResults() as $pipeline) {\n $pipelineStages = array_map(\n static function (PipelineStage $stage) {\n return [\n 'id' => $stage->getId(),\n 'label' => $stage->getLabel(),\n ];\n },\n $pipeline->getStages()\n );\n\n $stages = array_merge($stages, $pipelineStages);\n }\n\n return $stages;\n }\n\n public function fetchOpportunityPipelines(): array\n {\n $pipelines = [];\n\n try {\n $apiResponse = $this->makeRequest('/crm/v3/pipelines/deals');\n } catch (\\Exception $e) {\n $this->log->info('[Hubspot] Failed to fetch opportunity pipelines', [\n 'reason' => $e->getMessage(),\n ]);\n\n return [];\n }\n\n $response = $apiResponse->toArray();\n\n foreach ($response['results'] as $pipeline) {\n $pipelines[] = [\n 'id' => $pipeline['id'],\n 'label' => $pipeline['label'],\n ];\n }\n\n return $pipelines;\n }\n\n /**\n * @return array<CrmFieldOption>\n */\n public function fetchMeetingOutcomeFieldOptions(Field $field): array\n {\n return $field->getCrmProviderId() === 'meetingOutcome'\n ? $this->fetchMeetingOutcomeTypes()\n : $this->fetchCallActivityTypes();\n }\n\n public function fetchMeetingOutcomeTypes(): array\n {\n return $this->extractMeetingTypeOptions(\n 'https://api.hubapi.com/crm/v3/properties/meeting/hs_meeting_outcome'\n );\n }\n\n public function fetchCallActivityTypes(): array\n {\n return $this->extractMeetingTypeOptions(\n 'https://api.hubapi.com/crm/v3/properties/call/hs_activity_type'\n );\n }\n\n private function extractMeetingTypeOptions(string $endpoint): array\n {\n /** @var Response $response */\n $response = $this->getInstance()\n ->getClient()\n ->request('GET', $endpoint);\n\n /**\n * @var array<array{\n * value: string,\n * label: string,\n * displayOrder: int\n * }> $optionData\n */\n $optionData = $response->toArray()['options'] ?? [];\n\n $options = [];\n foreach ($optionData as $item) {\n $options[] = [\n 'id' => $item['value'],\n 'value' => $item['value'],\n 'label' => $item['label'],\n 'display_order' => $item['displayOrder'],\n ];\n }\n\n return $options;\n }\n\n /**\n * @return array<CrmFieldOption>\n */\n public function fetchDispositionFieldOptions(): array\n {\n $options = [];\n\n $dispositions = $this->fetchCallDispositions();\n\n foreach ($dispositions as $disposition) {\n if ($disposition['deleted'] !== false) {\n continue;\n }\n\n $option['value'] = $disposition['id'];\n $option['id'] = $disposition['id'];\n $option['label'] = $disposition['label'];\n\n $options[] = $option;\n }\n\n return $options;\n }\n\n /**\n * @return array<CrmFieldOption>\n */\n public function fetchOpportunityFieldOptions(Field $field): array\n {\n if ($field->isStageField()) {\n return $this->fetchOpportunityPipelineStages();\n }\n\n if ($field->isPipelineField()) {\n return $this->fetchOpportunityPipelines();\n }\n\n return $this->fetchPropertyOptions('deals', $field->getCrmProviderId());\n }\n\n /**\n * @throws BadRequest\n * @throws HubspotException\n */\n public function makeRequest(string $endpoint, $method = 'GET', $payload = [], ?string $queryString = null)\n {\n $endpoint = self::BASE_URL . $endpoint;\n\n if ($method === 'GET') {\n return $this->getInstance()->getClient()?->request(\n method: $method,\n endpoint: $endpoint,\n query_string: $queryString\n );\n } else {\n return $this->getInstance()->getClient()->request($method, $endpoint, [\n 'json' => ($payload),\n ]);\n }\n }\n\n /**\n * @throws BadRequest\n * @throws HubspotException\n */\n public function createMeeting(array $payload): Response\n {\n $endpoint = '/crm/v3/objects/meetings';\n\n return $this->makeRequest($endpoint, 'POST', $payload);\n }\n\n /**\n * @throws BadRequest\n * @throws HubspotException\n */\n public function updateMeeting(string $meetingId, array $payload): Response\n {\n $endpoint = '/crm/v3/objects/meetings/' . $meetingId;\n\n return $this->makeRequest($endpoint, 'PATCH', $payload);\n }\n\n /**\n * @throws \\Exception\n */\n public function createNote(\n string $body,\n string $ownerId,\n int $timestamp,\n string $objectId,\n NoteObject $noteObject\n ): ?string {\n try {\n $noteInput = new SimplePublicObjectInput([\n 'properties' => [\n 'hs_note_body' => $body,\n 'hubspot_owner_id' => $ownerId,\n 'hs_timestamp' => $timestamp,\n ],\n ]);\n\n // Create note\n $note = $this->getNewInstance()->crm()->objects()->basicApi()->create('note', $noteInput);\n\n $this->getNewInstance()->crm()->objects()->associationsApi()->create(\n 'note',\n $note->getId(),\n $this->getNoteObject($noteObject),\n $objectId,\n $this->getNoteAssociationType($noteObject),\n );\n\n return $note->getId();\n } catch (\\Exception $e) {\n $this->log->error('[Hubspot] Failed to create note', [\n 'objectId' => $objectId,\n 'noteObject' => $noteObject->getObjectType(),\n 'reason' => $e->getMessage(),\n ]);\n\n \\Sentry::captureException($e);\n }\n\n return null;\n }\n\n public function updateEngagement(string $objectId, array $engagement, array $metadata): void\n {\n $this->getInstance()->engagements()->update($objectId, $engagement, $metadata);\n }\n\n public function getEngagementData(string $engagementId): array\n {\n $engagement = $this->getInstance()->engagements()->get($engagementId);\n\n return $engagement->toArray();\n }\n\n public function createEngagement(array $engagement, array $associations, array $metadata): Response\n {\n return $this->getInstance()\n ->engagements()\n ->create($engagement, $associations, $metadata);\n }\n\n public function isUnauthorizedException(\\Exception $e): bool\n {\n // Check for specific HubSpot API exception types first\n if ($e instanceof BadRequest) {\n // BadRequest can contain 401 status codes\n return $e->getCode() === 401;\n }\n\n // Check for HTTP client exceptions with status codes\n if ($e instanceof \\GuzzleHttp\\Exception\\RequestException && $e->hasResponse()) {\n $response = $e->getResponse();\n if ($response !== null) {\n return $response->getStatusCode() === 401;\n }\n }\n\n // Check for Guzzle HTTP exceptions\n if ($e instanceof \\GuzzleHttp\\Exception\\ClientException) {\n return $e->getCode() === 401;\n }\n\n // Fallback to string matching as last resort, but be more specific\n $message = strtolower($e->getMessage());\n\n return str_contains($message, '401 unauthorized') ||\n str_contains($message, 'http 401') ||\n str_contains($message, 'status code 401') ||\n (preg_match('/\\b401\\b/', $message) && str_contains($message, 'unauthorized'));\n }\n\n /**\n * Validates and refreshes the access token if needed before API requests.\n * This ensures long-running processes don't fail due to token expiration.\n *\n * @throws SocialAccountTokenInvalidException\n */\n public function ensureValidToken(): void\n {\n if ($this->oauthAccount === null) {\n return;\n }\n\n $newToken = $this->tokenManager->ensureValidToken($this->oauthAccount);\n if ($newToken !== null) {\n $this->accessToken = $newToken;\n }\n }\n\n public function getConfig()\n {\n return $this->config;\n }\n\n // returns only active (archived=false)\n public function getOwners(): array\n {\n return $this->getNewInstance()->crm()->owners()->getAll();\n }\n\n /**\n * @param bool $archived\n *\n * @return array<Owner>|[]\n */\n public function getOwnersArchived(bool $archived = true): array\n {\n $endpoint = '/crm/v3/owners';\n $queryParams = [\n 'archived' => $archived ? 'true' : 'false',\n ];\n $queryString = http_build_query($queryParams);\n\n $owners = [];\n\n try {\n $response = $this->makeRequest(endpoint: $endpoint, queryString: $queryString);\n $responseData = $response?->toArray();\n\n foreach ($responseData['results'] as $result) {\n try {\n $owners[] = Owner::create($result);\n } catch (Throwable $e) {\n $this->log->error('[HubSpot] Failed to process owner data', [\n 'result' => $result,\n 'error' => $e->getMessage(),\n ]);\n\n continue;\n }\n }\n } catch (Throwable $e) {\n $this->log->error('HubSpot] Failed to fetch owners', [\n 'archived' => $archived,\n 'error' => $e->getMessage(),\n ]);\n\n return [];\n }\n\n return $owners;\n }\n\n public function getMeeting(string $engagementId): ObjectWithAssociations\n {\n return $this->getNewInstance()->crm()->objects()->basicApi()\n ->getById('meeting', $engagementId, null, 'contact,company,deal');\n }\n\n public function deleteEngagement(string $engagementId): void\n {\n $this->getInstance()->engagements()->delete((int) $engagementId);\n }\n\n public function getAssociationsData(array $ids, string $fromObject, string $toObject): array\n {\n $associationData = [];\n $idChunks = array_chunk($ids, self::ASSOCIATIONS_BATCH_SIZE_LIMIT);\n\n foreach ($idChunks as $idChunk) {\n try {\n $batchInput = new \\HubSpot\\Client\\Crm\\Associations\\Model\\BatchInputPublicObjectId();\n $batchInput->setInputs(array_map(function ($id) {\n $publicObjectId = new \\HubSpot\\Client\\Crm\\Associations\\Model\\PublicObjectId();\n $publicObjectId->setId($id);\n\n return $publicObjectId;\n }, $idChunk));\n\n $associatedObjectsData = $this\n ->getNewInstance()\n ->crm()\n ->associations()\n ->batchApi()\n ->read($fromObject, $toObject, $batchInput);\n\n if ($associatedObjectsData instanceof \\HubSpot\\Client\\Crm\\Associations\\Model\\BatchResponsePublicAssociationMulti) {\n foreach ($associatedObjectsData->getResults() as $association) {\n $from = $association->getFrom()->getId();\n $toAssociations = $association->getTo();\n\n if (! empty($toAssociations)) {\n $associationData[$from] = array_map(function ($item) {\n return $item->getId();\n }, $toAssociations);\n }\n }\n }\n } catch (\\Exception $e) {\n $this->log->error('[Hubspot] Failed to fetch associations', [\n 'from_object' => $fromObject,\n 'to_object' => $toObject,\n 'reason' => $e->getMessage(),\n ]);\n }\n }\n\n return $associationData;\n }\n\n /**\n * @throws \\Exception\n */\n private function getNoteAssociationType(NoteObject $noteObject): string\n {\n return match($noteObject) {\n NoteObject::Opportunity => 'note_to_deal',\n NoteObject::Lead, NoteObject::Contact => 'note_to_contact', // or 'note_to_lead' if your portal supports it\n NoteObject::Account => 'note_to_company',\n NoteObject::Call, NoteObject::Event => throw new \\Exception('Not supported'),\n };\n }\n\n /**\n * @throws \\Exception\n */\n private function getNoteObject(NoteObject $noteObject): string\n {\n return match($noteObject) {\n NoteObject::Opportunity => 'deal',\n NoteObject::Lead, NoteObject::Contact => 'contact',\n NoteObject::Account => 'company',\n NoteObject::Call, NoteObject::Event => throw new \\Exception('Not supported'),\n };\n }\n\n public function addAssociations(string $objectType, string $associationType, array $payload): Response\n {\n $endpoint = \"/crm/v4/associations/$objectType/$associationType/batch/create\";\n\n return $this->makeRequest($endpoint, 'POST', $payload);\n }\n\n public function removeAssociations(string $objectType, string $associationType, array $payload): Response\n {\n $endpoint = \"/crm/v4/associations/$objectType/$associationType/batch/archive\";\n\n return $this->makeRequest($endpoint, 'POST', $payload);\n }\n}","role_description":"text entry area","is_enabled":true,"is_focused":true,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Project","depth":3,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Project","depth":3,"bounds":{"left":0.011968086,"top":0.047885075,"width":0.024268618,"height":0.024740623},"on_screen":true,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"New File or Directory…","depth":4,"bounds":{"left":0.27027926,"top":1.0,"width":0.008643617,"height":0.0},"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Expand Selected","depth":4,"bounds":{"left":0.27027926,"top":1.0,"width":0.008643617,"height":0.0},"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Collapse All","depth":4,"bounds":{"left":0.27027926,"top":1.0,"width":0.008643617,"height":0.0},"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Options","depth":4,"bounds":{"left":0.27027926,"top":1.0,"width":0.008643617,"height":0.0},"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Hide","depth":4,"bounds":{"left":0.27027926,"top":1.0,"width":0.008643617,"height":0.0},"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false}]...
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# [PASSWORD_DOTS] HS [PASSWORD_DOTS]
select * from teams where id = 2; # 2
select * from features; # 2
select * from team_features where team_id = 2; # 2
select * from crm_configurations where id = 2; # 2
select * from users where team_id = 2; #
select * from playbooks where team_id = 2; # event 38
select * from playbook_categories where playbook_id = 38; #
SELECT * FROM activities WHERE crm_configuration_id = 2 and crm_provider_id is not null order by id desc;
[URL_WITH_CREDENTIALS] CrmFieldOption array{id:string, label:string, value?:string}
*/
class Client extends BaseClient implements HubspotClientInterface
{
public const string MIN_API_VERSION = '2';
public const string BASE_URL = '[URL_WITH_CREDENTIALS] T
* @param callable(): T $apiCall
* @return T
*
* @throws RateLimitException
*/
private function executeRequest(callable $apiCall)
{
if (! $this->rateLimiter->canMakeRequest($this->config)) {
$retryAfter = $this->rateLimiter->requestAvailableIn($this->config);
$this->log->warning('[Hubspot] Rate limit exceeded, deferring request', [
'team_id' => $this->config->team_id,
'config_id' => $this->config->getId(),
'retry_after' => $retryAfter,
]);
throw new RateLimitException(
'Hubspot rate limit reached for configuration ' . $this->config->getId(),
$retryAfter,
);
}
$this->rateLimiter->incrementRequestCount($this->config);
try {
return $apiCall();
} catch (Throwable $e) {
if ($this->isHubspotRateLimit($e)) {
$retryAfter = $this->parseRetryAfter($e);
$this->log->warning('[Hubspot] Received 429 from API', [
'team_id' => $this->config->team_id,
'config_id' => $this->config->getId(),
'retry_after' => $retryAfter,
'reason' => $e->getMessage(),
]);
throw new RateLimitException('Hubspot returned 429', $retryAfter, $e);
}
throw $e;
}
}
private function isHubspotRateLimit(Throwable $e): bool
{
return method_exists($e, 'getCode') && (int) $e->getCode() === 429;
}
private function parseRetryAfter(Throwable $e): int
{
if (method_exists($e, 'getResponseHeaders')) {
$headers = $e->getResponseHeaders() ?: [];
$value = $headers['Retry-After'] ?? $headers['retry-after'] ?? null;
if (is_array($value)) {
$value = $value[0] ?? null;
}
if (is_numeric($value)) {
return (int) $value;
}
}
return 10;
}
public function getMinimumApiVersion(): string
{
return self::MIN_API_VERSION;
}
public function getInstance(): Factory
{
return new Factory([
'key' => $this->accessToken,
'oauth2' => true,
'base_url' => $this->baseUrl,
]);
}
public function getNewInstance(): Discovery
{
return \HubSpot\Factory::createWithAccessToken($this->accessToken);
}
/**
* Secondly and daily limits for Hubspot API
*
* Product Tier: Free & Starter | Professional & Enterprise | API add-on (any tier)
* Burst: 100/10 seconds | 150/10 seconds | 200/10 seconds
* Daily: 250,000 | 500,000 | 1,000,000
*
* Official documentation states: The search endpoints are rate limited to five requests per second.
* Since with 5 RPS were still hitting secondly rate limits we lowered it to 4
*/
public function getPaginatedData(array $payload, string $type, int $offset = 0): array
{
$total = 0;
$lastId = null;
$rows = [];
foreach ($this->getPaginatedDataGenerator($payload, $type, $offset, $total, $lastId) as $row) {
$rows[] = $row;
}
return ['results' => $rows, 'total' => $total, 'last_record' => $lastId];
}
/**
* @throws HubspotException
* @throws SocialAccountTokenInvalidException
* @throws BadRequest
*/
public function getPaginatedDataGenerator(
array $payload,
string $type,
int $offset = 0,
int &$total = 0,
?string &$lastRecordId = null
): \Generator {
return $this->paginationService->getPaginatedDataGenerator(
$this,
$payload,
$type,
$offset,
$total,
$lastRecordId
);
}
/**
* @throws DealApiException
* @throws CrmException
*/
public function getOpportunityById(string $crmId, array $fields): array
{
try {
$deal = $this->executeRequest(fn () => $this->getNewInstance()->crm()->deals()->basicApi()->getById(
$crmId,
implode(',', $fields),
'companies,contacts'
));
} catch (DealApiException $e) {
$this->log->info('[Hubspot] Failed to fetch opportunity', [
'crm_id' => $crmId,
'reason' => $e->getMessage(),
]);
throw $e;
}
if (! $deal instanceof DealWithAssociations) {
throw new CrmException('Deal not found');
}
return [
'id' => $deal->getId(),
'properties' => $deal->getProperties(),
'associations' => $deal->getAssociations(),
];
}
/**
* Generic batch read method for HubSpot objects
*
* @param string $objectType The object type ('deals', 'companies', 'contacts')
* @param array<string> $crmIds Array of HubSpot object IDs (max 100)
* @param array<string> $fields Array of property names to fetch
*
* @return array<string, array> Array keyed by CRM ID with object data
*/
private function batchReadObjects(string $objectType, array $crmIds, array $fields): array
{
if (empty($crmIds)) {
return [];
}
$this->validateBatchSize($objectType, $crmIds);
$this->ensureValidToken();
try {
$batchConfig = $this->createBatchConfiguration($objectType);
$batchReadRequest = $this->prepareBatchRequest($batchConfig, $crmIds, $fields);
$response = $batchConfig['api']->read($batchReadRequest);
$this->validateApiResponse($response, $objectType);
$results = $this->processApiResults($response);
$this->logBatchResults($objectType, $crmIds, $results);
return $results;
} catch (\Throwable $e) {
$this->handleBatchError($e, $objectType, $crmIds);
}
}
private function validateBatchSize(string $objectType, array $crmIds): void
{
if (count($crmIds) > 100) {
throw new \InvalidArgumentException("Batch size cannot exceed 100 {$objectType}");
}
}
private function createBatchConfiguration(string $objectType): array
{
$configurations = [
'deals' => [
'batchReadRequest' => new \HubSpot\Client\Crm\Deals\Model\BatchReadInputSimplePublicObjectId(),
'inputClass' => \HubSpot\Client\Crm\Deals\Model\SimplePublicObjectId::class,
'api' => $this->getNewInstance()->crm()->deals()->batchApi(),
],
'companies' => [
'batchReadRequest' => new \HubSpot\Client\Crm\Companies\Model\BatchReadInputSimplePublicObjectId(),
'inputClass' => \HubSpot\Client\Crm\Companies\Model\SimplePublicObjectId::class,
'api' => $this->getNewInstance()->crm()->companies()->batchApi(),
],
'contacts' => [
'batchReadRequest' => new \HubSpot\Client\Crm\Contacts\Model\BatchReadInputSimplePublicObjectId(),
'inputClass' => \HubSpot\Client\Crm\Contacts\Model\SimplePublicObjectId::class,
'api' => $this->getNewInstance()->crm()->contacts()->batchApi(),
],
];
if (! isset($configurations[$objectType])) {
throw new \InvalidArgumentException("Unsupported object type: {$objectType}");
}
return $configurations[$objectType];
}
private function prepareBatchRequest(array $batchConfig, array $crmIds, array $fields): object
{
$batchReadRequest = $batchConfig['batchReadRequest'];
$inputClass = $batchConfig['inputClass'];
$inputs = array_map(function ($crmId) use ($inputClass) {
$input = new $inputClass();
$input->setId($crmId);
return $input;
}, $crmIds);
$batchReadRequest->setInputs($inputs);
$batchReadRequest->setProperties($fields);
return $batchReadRequest;
}
private function validateApiResponse($response, string $objectType): void
{
if (! $response) {
throw new CrmException("HubSpot API returned null response for {$objectType} batch read");
}
}
private function processApiResults($response): array
{
$results = [];
$responseResults = $response->getResults();
if ($responseResults) {
foreach ($responseResults as $object) {
if ($object && $object->getId()) {
$results[$object->getId()] = [
'id' => $object->getId(),
'properties' => $object->getProperties() ?: [],
];
}
}
}
return $results;
}
private function logBatchResults(string $objectType, array $crmIds, array $results): void
{
$this->log->info("[HubSpot] Batch fetched {$objectType}", [
'requested_count' => count($crmIds),
'returned_count' => count($results),
'crm_ids' => $crmIds,
]);
}
private function handleBatchError(\Throwable $e, string $objectType, array $crmIds): void
{
$errorMessage = $e->getMessage() ?: 'Unknown error';
$errorTrace = $e->getTraceAsString() ?: 'No trace available';
$this->log->error("[HubSpot] Failed to batch fetch {$objectType}", [
'crm_ids' => $crmIds,
'error' => $errorMessage,
'trace' => $errorTrace,
]);
throw new CrmException("Failed to batch fetch {$objectType}: " . $errorMessage);
}
/**
* Batch read multiple opportunities by their CRM IDs
*
* @param array<string> $crmIds Array of HubSpot deal IDs (max 100)
* @param array<string> $fields Array of property names to fetch
*
* @return array<string, array> Array keyed by CRM ID with opportunity data
*/
public function getOpportunitiesByIds(array $crmIds, array $fields): array
{
return $this->batchReadObjects('deals', $crmIds, $fields);
}
/**
* Batch read multiple companies by their CRM IDs
*
* @param array<string> $crmIds Array of HubSpot company IDs (max 100)
* @param array<string> $fields Array of property names to fetch
*
* @return array<string, array> Array keyed by CRM ID with company data
*/
public function getCompaniesByIds(array $crmIds, array $fields): array
{
return $this->batchReadObjects('companies', $crmIds, $fields);
}
/**
* Batch read multiple contacts by their CRM IDs
*
* @param array<string> $crmIds Array of HubSpot contact IDs (max 100)
* @param array<string> $fields Array of property names to fetch
*
* @return array<string, array> Array keyed by CRM ID with contact data
*/
public function getContactsByIds(array $crmIds, array $fields): array
{
return $this->batchReadObjects('contacts', $crmIds, $fields);
}
/**
* @throws CompanyApiException
* @throws CrmException
*/
public function getAccountById(string $crmId, array $fields): array
{
try {
$company = $this->getNewInstance()->crm()->companies()->basicApi()->getById(
$crmId,
implode(',', $fields),
);
} catch (CompanyApiException $e) {
$this->log->info('[Hubspot] Failed to fetch account', [
'crm_id' => $crmId,
'reason' => $e->getMessage(),
]);
throw $e;
}
if (! $company instanceof CompaniesWithAssociations) {
throw new CrmException('Account not found');
}
return [
'id' => $company->getId(),
'properties' => $company->getProperties(),
];
}
/**
* @throws ContactApiException
* @throws CrmException
*/
public function getContactById(string $crmId, array $fields): array
{
try {
$contact = $this->getNewInstance()->crm()->contacts()->basicApi()->getById(
$crmId,
implode(',', $fields)
);
} catch (ContactApiException $e) {
$this->log->info('[Hubspot] Failed to fetch contact', [
'crm_id' => $crmId,
'reason' => $e->getMessage(),
]);
throw $e;
}
if (! $contact instanceof ContactsWithAssociations) {
throw new CrmException('Contact not found');
}
return [
'id' => $contact->getId(),
'properties' => $contact->getProperties(),
];
}
/**
* This is email search request that Hubspot offers as GET (more generous quota)
*/
public function getContactByEmail(string $email, array $fields = []): array
{
try {
$contact = $this->getNewInstance()->crm()->contacts()->basicApi()->getById(
$email,
implode(',', $fields),
null,
false,
'email'
);
return [
'id' => $contact->getId(),
'properties' => $contact->getProperties(),
];
} catch (ContactApiException $e) {
$this->log->info('[Hubspot] Failed to fetch contact', [
'email' => $email,
'reason' => $e->getMessage(),
]);
return [];
}
}
/**
* @throws CrmException
*/
public function fetchProperty(string $objectType, string $propertyId): Property
{
$result = $this->getNewInstance()->crm()->properties()->coreApi()->getByName($objectType, $propertyId);
if (! $result instanceof Property) {
$this->log->error('[Hubspot] Failed to fetch property', [
'object_type' => $objectType,
'property_id' => $propertyId,
'reason' => $result->getMessage(),
]);
throw new CrmException('Failed to fetch property');
}
return $result;
}
/**
* @return array<CrmFieldOption>
*/
public function fetchPropertyOptions(string $objectType, string $propertyId): array
{
/** @var array<CrmFieldOption> */
return $this->fetchProperty($objectType, $propertyId)->getOptions();
}
/**
* @return array<array{id:string, label:string, deleted:bool}>
*/
public function fetchCallDispositions(): array
{
/** @var Response $response */
$response = $this->getInstance()->engagements()->getCallDispositions();
/**
* @var array<array{
* id:string,
* label:string,
* deleted: bool
* }>
*/
return $response->toArray();
}
/**
* @return array<CrmFieldOption>
*/
public function fetchOpportunityPipelineStages(): array
{
$stages = [];
$apiResponse = $this->getNewInstance()->crm()->pipelines()->pipelinesApi()->getAll('deals');
if ($apiResponse instanceof Error) {
$this->log->error('[Hubspot] Failed to fetch opportunity pipelines', [
'reason' => $apiResponse->getMessage(),
]);
return [];
}
foreach ($apiResponse->getResults() as $pipeline) {
$pipelineStages = array_map(
static function (PipelineStage $stage) {
return [
'id' => $stage->getId(),
'label' => $stage->getLabel(),
];
},
$pipeline->getStages()
);
$stages = array_merge($stages, $pipelineStages);
}
return $stages;
}
public function fetchOpportunityPipelines(): array
{
$pipelines = [];
try {
$apiResponse = $this->makeRequest('/crm/v3/pipelines/deals');
} catch (\Exception $e) {
$this->log->info('[Hubspot] Failed to fetch opportunity pipelines', [
'reason' => $e->getMessage(),
]);
return [];
}
$response = $apiResponse->toArray();
foreach ($response['results'] as $pipeline) {
$pipelines[] = [
'id' => $pipeline['id'],
'label' => $pipeline['label'],
];
}
return $pipelines;
}
/**
* @return array<CrmFieldOption>
*/
public function fetchMeetingOutcomeFieldOptions(Field $field): array
{
return $field->getCrmProviderId() === 'meetingOutcome'
? $this->fetchMeetingOutcomeTypes()
: $this->fetchCallActivityTypes();
}
public function fetchMeetingOutcomeTypes(): array
{
return $this->extractMeetingTypeOptions(
'[URL_WITH_CREDENTIALS] Response $response */
$response = $this->getInstance()
->getClient()
->request('GET', $endpoint);
/**
* @var array<array{
* value: string,
* label: string,
* displayOrder: int
* }> $optionData
*/
$optionData = $response->toArray()['options'] ?? [];
$options = [];
foreach ($optionData as $item) {
$options[] = [
'id' => $item['value'],
'value' => $item['value'],
'label' => $item['label'],
'display_order' => $item['displayOrder'],
];
}
return $options;
}
/**
* @return array<CrmFieldOption>
*/
public function fetchDispositionFieldOptions(): array
{
$options = [];
$dispositions = $this->fetchCallDispositions();
foreach ($dispositions as $disposition) {
if ($disposition['deleted'] !== false) {
continue;
}
$option['value'] = $disposition['id'];
$option['id'] = $disposition['id'];
$option['label'] = $disposition['label'];
$options[] = $option;
}
return $options;
}
/**
* @return array<CrmFieldOption>
*/
public function fetchOpportunityFieldOptions(Field $field): array
{
if ($field->isStageField()) {
return $this->fetchOpportunityPipelineStages();
}
if ($field->isPipelineField()) {
return $this->fetchOpportunityPipelines();
}
return $this->fetchPropertyOptions('deals', $field->getCrmProviderId());
}
/**
* @throws BadRequest
* @throws HubspotException
*/
public function makeRequest(string $endpoint, $method = 'GET', $payload = [], ?string $queryString = null)
{
$endpoint = self::BASE_URL . $endpoint;
if ($method === 'GET') {
return $this->getInstance()->getClient()?->request(
method: $method,
endpoint: $endpoint,
query_string: $queryString
);
} else {
return $this->getInstance()->getClient()->request($method, $endpoint, [
'json' => ($payload),
]);
}
}
/**
* @throws BadRequest
* @throws HubspotException
*/
public function createMeeting(array $payload): Response
{
$endpoint = '/crm/v3/objects/meetings';
return $this->makeRequest($endpoint, 'POST', $payload);
}
/**
* @throws BadRequest
* @throws HubspotException
*/
public function updateMeeting(string $meetingId, array $payload): Response
{
$endpoint = '/crm/v3/objects/meetings/' . $meetingId;
return $this->makeRequest($endpoint, 'PATCH', $payload);
}
/**
* @throws \Exception
*/
public function createNote(
string $body,
string $ownerId,
int $timestamp,
string $objectId,
NoteObject $noteObject
): ?string {
try {
$noteInput = new SimplePublicObjectInput([
'properties' => [
'hs_note_body' => $body,
'hubspot_owner_id' => $ownerId,
'hs_timestamp' => $timestamp,
],
]);
// Create note
$note = $this->getNewInstance()->crm()->objects()->basicApi()->create('note', $noteInput);
$this->getNewInstance()->crm()->objects()->associationsApi()->create(
'note',
$note->getId(),
$this->getNoteObject($noteObject),
$objectId,
$this->getNoteAssociationType($noteObject),
);
return $note->getId();
} catch (\Exception $e) {
$this->log->error('[Hubspot] Failed to create note', [
'objectId' => $objectId,
'noteObject' => $noteObject->getObjectType(),
'reason' => $e->getMessage(),
]);
\Sentry::captureException($e);
}
return null;
}
public function updateEngagement(string $objectId, array $engagement, array $metadata): void
{
$this->getInstance()->engagements()->update($objectId, $engagement, $metadata);
}
public function getEngagementData(string $engagementId): array
{
$engagement = $this->getInstance()->engagements()->get($engagementId);
return $engagement->toArray();
}
public function createEngagement(array $engagement, array $associations, array $metadata): Response
{
return $this->getInstance()
->engagements()
->create($engagement, $associations, $metadata);
}
public function isUnauthorizedException(\Exception $e): bool
{
// Check for specific HubSpot API exception types first
if ($e instanceof BadRequest) {
// BadRequest can contain 401 status codes
return $e->getCode() === 401;
}
// Check for HTTP client exceptions with status codes
if ($e instanceof \GuzzleHttp\Exception\RequestException && $e->hasResponse()) {
$response = $e->getResponse();
if ($response !== null) {
return $response->getStatusCode() === 401;
}
}
// Check for Guzzle HTTP exceptions
if ($e instanceof \GuzzleHttp\Exception\ClientException) {
return $e->getCode() === 401;
}
// Fallback to string matching as last resort, but be more specific
$message = strtolower($e->getMessage());
return str_contains($message, '401 unauthorized') ||
str_contains($message, 'http 401') ||
str_contains($message, 'status code 401') ||
(preg_match('/\b401\b/', $message) && str_contains($message, 'unauthorized'));
}
/**
* Validates and refreshes the access token if needed before API requests.
* This ensures long-running processes don't fail due to token expiration.
*
* @throws SocialAccountTokenInvalidException
*/
public function ensureValidToken(): void
{
if ($this->oauthAccount === null) {
return;
}
$newToken = $this->tokenManager->ensureValidToken($this->oauthAccount);
if ($newToken !== null) {
$this->accessToken = $newToken;
}
}
public function getConfig()
{
return $this->config;
}
// returns only active (archived=false)
public function getOwners(): array
{
return $this->getNewInstance()->crm()->owners()->getAll();
}
/**
* @param bool $archived
*
* @return array<Owner>|[]
*/
public function getOwnersArchived(bool $archived = true): array
{
$endpoint = '/crm/v3/owners';
$queryParams = [
'archived' => $archived ? 'true' : 'false',
];
$queryString = http_build_query($queryParams);
$owners = [];
try {
$response = $this->makeRequest(endpoint: $endpoint, queryString: $queryString);
$responseData = $response?->toArray();
foreach ($responseData['results'] as $result) {
try {
$owners[] = Owner::create($result);
} catch (Throwable $e) {
$this->log->error('[HubSpot] Failed to process owner data', [
'result' => $result,
'error' => $e->getMessage(),
]);
continue;
}
}
} catch (Throwable $e) {
$this->log->error('HubSpot] Failed to fetch owners', [
'archived' => $archived,
'error' => $e->getMessage(),
]);
return [];
}
return $owners;
}
public function getMeeting(string $engagementId): ObjectWithAssociations
{
return $this->getNewInstance()->crm()->objects()->basicApi()
->getById('meeting', $engagementId, null, 'contact,company,deal');
}
public function deleteEngagement(string $engagementId): void
{
$this->getInstance()->engagements()->delete((int) $engagementId);
}
public function getAssociationsData(array $ids, string $fromObject, string $toObject): array
{
$associationData = [];
$idChunks = array_chunk($ids, self::ASSOCIATIONS_BATCH_SIZE_LIMIT);
foreach ($idChunks as $idChunk) {
try {
$batchInput = new \HubSpot\Client\Crm\Associations\Model\BatchInputPublicObjectId();
$batchInput->setInputs(array_map(function ($id) {
$publicObjectId = new \HubSpot\Client\Crm\Associations\Model\PublicObjectId();
$publicObjectId->setId($id);
return $publicObjectId;
}, $idChunk));
$associatedObjectsData = $this
->getNewInstance()
->crm()
->associations()
->batchApi()
->read($fromObject, $toObject, $batchInput);
if ($associatedObjectsData instanceof \HubSpot\Client\Crm\Associations\Model\BatchResponsePublicAssociationMulti) {
foreach ($associatedObjectsData->getResults() as $association) {
$from = $association->getFrom()->getId();
$toAssociations = $association->getTo();
if (! empty($toAssociations)) {
$associationData[$from] = array_map(function ($item) {
return $item->getId();
}, $toAssociations);
}
}
}
} catch (\Exception $e) {
$this->log->error('[Hubspot] Failed to fetch associations', [
'from_object' => $fromObject,
'to_object' => $toObject,
'reason' => $e->getMessage(),
]);
}
}
return $associationData;
}
/**
* @throws \Exception
*/
private function getNoteAssociationType(NoteObject $noteObject): string
{
return match($noteObject) {
NoteObject::Opportunity => 'note_to_deal',
NoteObject::Lead, NoteObject::Contact => 'note_to_contact', // or 'note_to_lead' if your portal supports it
NoteObject::Account => 'note_to_company',
NoteObject::Call, NoteObject::Event => throw new \Exception('Not supported'),
};
}
/**
* @throws \Exception
*/
private function getNoteObject(NoteObject $noteObject): string
{
return match($noteObject) {
NoteObject::Opportunity => 'deal',
NoteObject::Lead, NoteObject::Contact => 'contact',
NoteObject::Account => 'company',
NoteObject::Call, NoteObject::Event => throw new \Exception('Not supported'),
};
}
public function addAssociations(string $objectType, string $associationType, array $payload): Response
{
$endpoint = "/crm/v4/associations/$objectType/$associationType/batch/create";
return $this->makeRequest($endpoint, 'POST', $payload);
}
public function removeAssociations(string $objectType, string $associationType, array $payload): Response
{
$endpoint = "/crm/v4/associations/$objectType/$associationType/batch/archive";
return $this->makeRequest($endpoint, 'POST', $payload);
}
}
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/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778153403632_m2.jpg...
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ma
Inherited members (⌘R)
Anonymous Classes (⌘I)
L ma
Inherited members (⌘R)
Anonymous Classes (⌘I)
Lambdas (⌘L)
Client, class
MIN_API_VERSION: string = '2' ↑BaseClient, public
tokenManager: HubspotTokenManager, private
__construct(socialAccountService: SocialAccountService, paginationService: HubspotPaginationService, tokenManager: HubspotTokenManager, rateLimiter: ProviderRateLimiter) ↑BaseClient, public method
getContactByEmail(email: string, [fields: array = [...]]): array, public method
getMinimumApiVersion(): string ↑ClientInterface, public method
makeRequest(endpoint: string, [method: string = 'GET'], [payload: array = [...]], [queryString: null|string = null]): null|ResponseInterface|Response, public method
MIN_API_VERSION: string = '2' ↑BaseClient, public
tokenManager: HubspotTokenManager, private
__construct(socialAccountService: SocialAccountService, paginationService: HubspotPaginationService, tokenManager: HubspotTokenManager, rateLimiter: ProviderRateLimiter) ↑BaseClient, public method
getContactByEmail(email: string, [fields: array = [...]]): array, public method
getMinimumApiVersion(): string ↑ClientInterface, public method
makeRequest(endpoint: string, [method: string = 'GET'], [payload: array = [...]], [queryString: null|string = null]): null|ResponseInterface|Response, public method
Client.php...
|
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Client, class
MIN_API_VERSION: string = '2' ↑BaseClient, public
tokenManager: HubspotTokenManager, private
__construct(socialAccountService: SocialAccountService, paginationService: HubspotPaginationService, tokenManager: HubspotTokenManager, rateLimiter: ProviderRateLimiter) ↑BaseClient, public method
getContactByEmail(email: string, [fields: array = [...]]): array, public method
getMinimumApiVersion(): string ↑ClientInterface, public method
makeRequest(endpoint: string, [method: string = 'GET'], [payload: array = [...]], [queryString: null|string = null]): null|ResponseInterface|Response, public method
MIN_API_VERSION: string = '2' ↑BaseClient, public
tokenManager: HubspotTokenManager, private
__construct(socialAccountService: SocialAccountService, paginationService: HubspotPaginationService, tokenManager: HubspotTokenManager, rateLimiter: ProviderRateLimiter) ↑BaseClient, public method
getContactByEmail(email: string, [fields: array = [...]]): array, public method
getMinimumApiVersion(): string ↑ClientInterface, public method
makeRequest(endpoint: string, [method: string = 'GET'], [payload: array = [...]], [queryString: null|string = null]): null|ResponseInterface|Response, public method
Client.php...
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faVsco.js – Hubspot/Client.php
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Project: faVsco.js, menu
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# [PASSWORD_DOTS] HS [PASSWORD_DOTS]
select * from teams where id = 2; # 2
select * from features; # 2
select * from team_features where team_id = 2; # 2
select * from crm_configurations where id = 2; # 2
select * from users where team_id = 2; #
select * from playbooks where team_id = 2; # event 38
select * from playbook_categories where playbook_id = 38; #
SELECT * FROM activities WHERE crm_configuration_id = 2 and crm_provider_id is not null order by id desc;
[URL_WITH_CREDENTIALS] CrmFieldOption array{id:string, label:string, value?:string}
*/
class Client extends BaseClient implements HubspotClientInterface
{
public const string MIN_API_VERSION = '2';
public const string BASE_URL = '[URL_WITH_CREDENTIALS] T
* @param callable(): T $apiCall
* @return T
*
* @throws RateLimitException
*/
private function executeRequest(callable $apiCall)
{
if (! $this->rateLimiter->canMakeRequest($this->config)) {
$retryAfter = $this->rateLimiter->requestAvailableIn($this->config);
$this->log->warning('[Hubspot] Rate limit exceeded, deferring request', [
'team_id' => $this->config->team_id,
'config_id' => $this->config->getId(),
'retry_after' => $retryAfter,
]);
throw new RateLimitException(
'Hubspot rate limit reached for configuration ' . $this->config->getId(),
$retryAfter,
);
}
$this->rateLimiter->incrementRequestCount($this->config);
try {
return $apiCall();
} catch (Throwable $e) {
if ($this->isHubspotRateLimit($e)) {
$retryAfter = $this->parseRetryAfter($e);
$this->log->warning('[Hubspot] Received 429 from API', [
'team_id' => $this->config->team_id,
'config_id' => $this->config->getId(),
'retry_after' => $retryAfter,
'reason' => $e->getMessage(),
]);
throw new RateLimitException('Hubspot returned 429', $retryAfter, $e);
}
throw $e;
}
}
private function isHubspotRateLimit(Throwable $e): bool
{
return method_exists($e, 'getCode') && (int) $e->getCode() === 429;
}
private function parseRetryAfter(Throwable $e): int
{
if (method_exists($e, 'getResponseHeaders')) {
$headers = $e->getResponseHeaders() ?: [];
$value = $headers['Retry-After'] ?? $headers['retry-after'] ?? null;
if (is_array($value)) {
$value = $value[0] ?? null;
}
if (is_numeric($value)) {
return (int) $value;
}
}
return 10;
}
public function getMinimumApiVersion(): string
{
return self::MIN_API_VERSION;
}
public function getInstance(): Factory
{
return new Factory([
'key' => $this->accessToken,
'oauth2' => true,
'base_url' => $this->baseUrl,
]);
}
public function getNewInstance(): Discovery
{
return \HubSpot\Factory::createWithAccessToken($this->accessToken);
}
/**
* Secondly and daily limits for Hubspot API
*
* Product Tier: Free & Starter | Professional & Enterprise | API add-on (any tier)
* Burst: 100/10 seconds | 150/10 seconds | 200/10 seconds
* Daily: 250,000 | 500,000 | 1,000,000
*
* Official documentation states: The search endpoints are rate limited to five requests per second.
* Since with 5 RPS were still hitting secondly rate limits we lowered it to 4
*/
public function getPaginatedData(array $payload, string $type, int $offset = 0): array
{
$total = 0;
$lastId = null;
$rows = [];
foreach ($this->getPaginatedDataGenerator($payload, $type, $offset, $total, $lastId) as $row) {
$rows[] = $row;
}
return ['results' => $rows, 'total' => $total, 'last_record' => $lastId];
}
/**
* @throws HubspotException
* @throws SocialAccountTokenInvalidException
* @throws BadRequest
*/
public function getPaginatedDataGenerator(
array $payload,
string $type,
int $offset = 0,
int &$total = 0,
?string &$lastRecordId = null
): \Generator {
return $this->paginationService->getPaginatedDataGenerator(
$this,
$payload,
$type,
$offset,
$total,
$lastRecordId
);
}
/**
* @throws DealApiException
* @throws CrmException
*/
public function getOpportunityById(string $crmId, array $fields): array
{
try {
$deal = $this->executeRequest(fn () => $this->getNewInstance()->crm()->deals()->basicApi()->getById(
$crmId,
implode(',', $fields),
'companies,contacts'
));
} catch (DealApiException $e) {
$this->log->info('[Hubspot] Failed to fetch opportunity', [
'crm_id' => $crmId,
'reason' => $e->getMessage(),
]);
throw $e;
}
if (! $deal instanceof DealWithAssociations) {
throw new CrmException('Deal not found');
}
return [
'id' => $deal->getId(),
'properties' => $deal->getProperties(),
'associations' => $deal->getAssociations(),
];
}
/**
* Generic batch read method for HubSpot objects
*
* @param string $objectType The object type ('deals', 'companies', 'contacts')
* @param array<string> $crmIds Array of HubSpot object IDs (max 100)
* @param array<string> $fields Array of property names to fetch
*
* @return array<string, array> Array keyed by CRM ID with object data
*/
private function batchReadObjects(string $objectType, array $crmIds, array $fields): array
{
if (empty($crmIds)) {
return [];
}
$this->validateBatchSize($objectType, $crmIds);
$this->ensureValidToken();
try {
$batchConfig = $this->createBatchConfiguration($objectType);
$batchReadRequest = $this->prepareBatchRequest($batchConfig, $crmIds, $fields);
$response = $batchConfig['api']->read($batchReadRequest);
$this->validateApiResponse($response, $objectType);
$results = $this->processApiResults($response);
$this->logBatchResults($objectType, $crmIds, $results);
return $results;
} catch (\Throwable $e) {
$this->handleBatchError($e, $objectType, $crmIds);
}
}
private function validateBatchSize(string $objectType, array $crmIds): void
{
if (count($crmIds) > 100) {
throw new \InvalidArgumentException("Batch size cannot exceed 100 {$objectType}");
}
}
private function createBatchConfiguration(string $objectType): array
{
$configurations = [
'deals' => [
'batchReadRequest' => new \HubSpot\Client\Crm\Deals\Model\BatchReadInputSimplePublicObjectId(),
'inputClass' => \HubSpot\Client\Crm\Deals\Model\SimplePublicObjectId::class,
'api' => $this->getNewInstance()->crm()->deals()->batchApi(),
],
'companies' => [
'batchReadRequest' => new \HubSpot\Client\Crm\Companies\Model\BatchReadInputSimplePublicObjectId(),
'inputClass' => \HubSpot\Client\Crm\Companies\Model\SimplePublicObjectId::class,
'api' => $this->getNewInstance()->crm()->companies()->batchApi(),
],
'contacts' => [
'batchReadRequest' => new \HubSpot\Client\Crm\Contacts\Model\BatchReadInputSimplePublicObjectId(),
'inputClass' => \HubSpot\Client\Crm\Contacts\Model\SimplePublicObjectId::class,
'api' => $this->getNewInstance()->crm()->contacts()->batchApi(),
],
];
if (! isset($configurations[$objectType])) {
throw new \InvalidArgumentException("Unsupported object type: {$objectType}");
}
return $configurations[$objectType];
}
private function prepareBatchRequest(array $batchConfig, array $crmIds, array $fields): object
{
$batchReadRequest = $batchConfig['batchReadRequest'];
$inputClass = $batchConfig['inputClass'];
$inputs = array_map(function ($crmId) use ($inputClass) {
$input = new $inputClass();
$input->setId($crmId);
return $input;
}, $crmIds);
$batchReadRequest->setInputs($inputs);
$batchReadRequest->setProperties($fields);
return $batchReadRequest;
}
private function validateApiResponse($response, string $objectType): void
{
if (! $response) {
throw new CrmException("HubSpot API returned null response for {$objectType} batch read");
}
}
private function processApiResults($response): array
{
$results = [];
$responseResults = $response->getResults();
if ($responseResults) {
foreach ($responseResults as $object) {
if ($object && $object->getId()) {
$results[$object->getId()] = [
'id' => $object->getId(),
'properties' => $object->getProperties() ?: [],
];
}
}
}
return $results;
}
private function logBatchResults(string $objectType, array $crmIds, array $results): void
{
$this->log->info("[HubSpot] Batch fetched {$objectType}", [
'requested_count' => count($crmIds),
'returned_count' => count($results),
'crm_ids' => $crmIds,
]);
}
private function handleBatchError(\Throwable $e, string $objectType, array $crmIds): void
{
$errorMessage = $e->getMessage() ?: 'Unknown error';
$errorTrace = $e->getTraceAsString() ?: 'No trace available';
$this->log->error("[HubSpot] Failed to batch fetch {$objectType}", [
'crm_ids' => $crmIds,
'error' => $errorMessage,
'trace' => $errorTrace,
]);
throw new CrmException("Failed to batch fetch {$objectType}: " . $errorMessage);
}
/**
* Batch read multiple opportunities by their CRM IDs
*
* @param array<string> $crmIds Array of HubSpot deal IDs (max 100)
* @param array<string> $fields Array of property names to fetch
*
* @return array<string, array> Array keyed by CRM ID with opportunity data
*/
public function getOpportunitiesByIds(array $crmIds, array $fields): array
{
return $this->batchReadObjects('deals', $crmIds, $fields);
}
/**
* Batch read multiple companies by their CRM IDs
*
* @param array<string> $crmIds Array of HubSpot company IDs (max 100)
* @param array<string> $fields Array of property names to fetch
*
* @return array<string, array> Array keyed by CRM ID with company data
*/
public function getCompaniesByIds(array $crmIds, array $fields): array
{
return $this->batchReadObjects('companies', $crmIds, $fields);
}
/**
* Batch read multiple contacts by their CRM IDs
*
* @param array<string> $crmIds Array of HubSpot contact IDs (max 100)
* @param array<string> $fields Array of property names to fetch
*
* @return array<string, array> Array keyed by CRM ID with contact data
*/
public function getContactsByIds(array $crmIds, array $fields): array
{
return $this->batchReadObjects('contacts', $crmIds, $fields);
}
/**
* @throws CompanyApiException
* @throws CrmException
*/
public function getAccountById(string $crmId, array $fields): array
{
try {
$company = $this->getNewInstance()->crm()->companies()->basicApi()->getById(
$crmId,
implode(',', $fields),
);
} catch (CompanyApiException $e) {
$this->log->info('[Hubspot] Failed to fetch account', [
'crm_id' => $crmId,
'reason' => $e->getMessage(),
]);
throw $e;
}
if (! $company instanceof CompaniesWithAssociations) {
throw new CrmException('Account not found');
}
return [
'id' => $company->getId(),
'properties' => $company->getProperties(),
];
}
/**
* @throws ContactApiException
* @throws CrmException
*/
public function getContactById(string $crmId, array $fields): array
{
try {
$contact = $this->getNewInstance()->crm()->contacts()->basicApi()->getById(
$crmId,
implode(',', $fields)
);
} catch (ContactApiException $e) {
$this->log->info('[Hubspot] Failed to fetch contact', [
'crm_id' => $crmId,
'reason' => $e->getMessage(),
]);
throw $e;
}
if (! $contact instanceof ContactsWithAssociations) {
throw new CrmException('Contact not found');
}
return [
'id' => $contact->getId(),
'properties' => $contact->getProperties(),
];
}
/**
* This is email search request that Hubspot offers as GET (more generous quota)
*/
public function getContactByEmail(string $email, array $fields = []): array
{
try {
$contact = $this->getNewInstance()->crm()->contacts()->basicApi()->getById(
$email,
implode(',', $fields),
null,
false,
'email'
);
return [
'id' => $contact->getId(),
'properties' => $contact->getProperties(),
];
} catch (ContactApiException $e) {
$this->log->info('[Hubspot] Failed to fetch contact', [
'email' => $email,
'reason' => $e->getMessage(),
]);
return [];
}
}
/**
* @throws CrmException
*/
public function fetchProperty(string $objectType, string $propertyId): Property
{
$result = $this->getNewInstance()->crm()->properties()->coreApi()->getByName($objectType, $propertyId);
if (! $result instanceof Property) {
$this->log->error('[Hubspot] Failed to fetch property', [
'object_type' => $objectType,
'property_id' => $propertyId,
'reason' => $result->getMessage(),
]);
throw new CrmException('Failed to fetch property');
}
return $result;
}
/**
* @return array<CrmFieldOption>
*/
public function fetchPropertyOptions(string $objectType, string $propertyId): array
{
/** @var array<CrmFieldOption> */
return $this->fetchProperty($objectType, $propertyId)->getOptions();
}
/**
* @return array<array{id:string, label:string, deleted:bool}>
*/
public function fetchCallDispositions(): array
{
/** @var Response $response */
$response = $this->getInstance()->engagements()->getCallDispositions();
/**
* @var array<array{
* id:string,
* label:string,
* deleted: bool
* }>
*/
return $response->toArray();
}
/**
* @return array<CrmFieldOption>
*/
public function fetchOpportunityPipelineStages(): array
{
$stages = [];
$apiResponse = $this->getNewInstance()->crm()->pipelines()->pipelinesApi()->getAll('deals');
if ($apiResponse instanceof Error) {
$this->log->error('[Hubspot] Failed to fetch opportunity pipelines', [
'reason' => $apiResponse->getMessage(),
]);
return [];
}
foreach ($apiResponse->getResults() as $pipeline) {
$pipelineStages = array_map(
static function (PipelineStage $stage) {
return [
'id' => $stage->getId(),
'label' => $stage->getLabel(),
];
},
$pipeline->getStages()
);
$stages = array_merge($stages, $pipelineStages);
}
return $stages;
}
public function fetchOpportunityPipelines(): array
{
$pipelines = [];
try {
$apiResponse = $this->makeRequest('/crm/v3/pipelines/deals');
} catch (\Exception $e) {
$this->log->info('[Hubspot] Failed to fetch opportunity pipelines', [
'reason' => $e->getMessage(),
]);
return [];
}
$response = $apiResponse->toArray();
foreach ($response['results'] as $pipeline) {
$pipelines[] = [
'id' => $pipeline['id'],
'label' => $pipeline['label'],
];
}
return $pipelines;
}
/**
* @return array<CrmFieldOption>
*/
public function fetchMeetingOutcomeFieldOptions(Field $field): array
{
return $field->getCrmProviderId() === 'meetingOutcome'
? $this->fetchMeetingOutcomeTypes()
: $this->fetchCallActivityTypes();
}
public function fetchMeetingOutcomeTypes(): array
{
return $this->extractMeetingTypeOptions(
'[URL_WITH_CREDENTIALS] Response $response */
$response = $this->getInstance()
->getClient()
->request('GET', $endpoint);
/**
* @var array<array{
* value: string,
* label: string,
* displayOrder: int
* }> $optionData
*/
$optionData = $response->toArray()['options'] ?? [];
$options = [];
foreach ($optionData as $item) {
$options[] = [
'id' => $item['value'],
'value' => $item['value'],
'label' => $item['label'],
'display_order' => $item['displayOrder'],
];
}
return $options;
}
/**
* @return array<CrmFieldOption>
*/
public function fetchDispositionFieldOptions(): array
{
$options = [];
$dispositions = $this->fetchCallDispositions();
foreach ($dispositions as $disposition) {
if ($disposition['deleted'] !== false) {
continue;
}
$option['value'] = $disposition['id'];
$option['id'] = $disposition['id'];
$option['label'] = $disposition['label'];
$options[] = $option;
}
return $options;
}
/**
* @return array<CrmFieldOption>
*/
public function fetchOpportunityFieldOptions(Field $field): array
{
if ($field->isStageField()) {
return $this->fetchOpportunityPipelineStages();
}
if ($field->isPipelineField()) {
return $this->fetchOpportunityPipelines();
}
return $this->fetchPropertyOptions('deals', $field->getCrmProviderId());
}
/**
* @throws BadRequest
* @throws HubspotException
*/
public function makeRequest(string $endpoint, $method = 'GET', $payload = [], ?string $queryString = null)
{
$endpoint = self::BASE_URL . $endpoint;
if ($method === 'GET') {
return $this->getInstance()->getClient()?->request(
method: $method,
endpoint: $endpoint,
query_string: $queryString
);
} else {
return $this->getInstance()->getClient()->request($method, $endpoint, [
'json' => ($payload),
]);
}
}
/**
* @throws BadRequest
* @throws HubspotException
*/
public function createMeeting(array $payload): Response
{
$endpoint = '/crm/v3/objects/meetings';
return $this->makeRequest($endpoint, 'POST', $payload);
}
/**
* @throws BadRequest
* @throws HubspotException
*/
public function updateMeeting(string $meetingId, array $payload): Response
{
$endpoint = '/crm/v3/objects/meetings/' . $meetingId;
return $this->makeRequest($endpoint, 'PATCH', $payload);
}
/**
* @throws \Exception
*/
public function createNote(
string $body,
string $ownerId,
int $timestamp,
string $objectId,
NoteObject $noteObject
): ?string {
try {
$noteInput = new SimplePublicObjectInput([
'properties' => [
'hs_note_body' => $body,
'hubspot_owner_id' => $ownerId,
'hs_timestamp' => $timestamp,
],
]);
// Create note
$note = $this->getNewInstance()->crm()->objects()->basicApi()->create('note', $noteInput);
$this->getNewInstance()->crm()->objects()->associationsApi()->create(
'note',
$note->getId(),
$this->getNoteObject($noteObject),
$objectId,
$this->getNoteAssociationType($noteObject),
);
return $note->getId();
} catch (\Exception $e) {
$this->log->error('[Hubspot] Failed to create note', [
'objectId' => $objectId,
'noteObject' => $noteObject->getObjectType(),
'reason' => $e->getMessage(),
]);
\Sentry::captureException($e);
}
return null;
}
public function updateEngagement(string $objectId, array $engagement, array $metadata): void
{
$this->getInstance()->engagements()->update($objectId, $engagement, $metadata);
}
public function getEngagementData(string $engagementId): array
{
$engagement = $this->getInstance()->engagements()->get($engagementId);
return $engagement->toArray();
}
public function createEngagement(array $engagement, array $associations, array $metadata): Response
{
return $this->getInstance()
->engagements()
->create($engagement, $associations, $metadata);
}
public function isUnauthorizedException(\Exception $e): bool
{
// Check for specific HubSpot API exception types first
if ($e instanceof BadRequest) {
// BadRequest can contain 401 status codes
return $e->getCode() === 401;
}
// Check for HTTP client exceptions with status codes
if ($e instanceof \GuzzleHttp\Exception\RequestException && $e->hasResponse()) {
$response = $e->getResponse();
if ($response !== null) {
return $response->getStatusCode() === 401;
}
}
// Check for Guzzle HTTP exceptions
if ($e instanceof \GuzzleHttp\Exception\ClientException) {
return $e->getCode() === 401;
}
// Fallback to string matching as last resort, but be more specific
$message = strtolower($e->getMessage());
return str_contains($message, '401 unauthorized') ||
str_contains($message, 'http 401') ||
str_contains($message, 'status code 401') ||
(preg_match('/\b401\b/', $message) && str_contains($message, 'unauthorized'));
}
/**
* Validates and refreshes the access token if needed before API requests.
* This ensures long-running processes don't fail due to token expiration.
*
* @throws SocialAccountTokenInvalidException
*/
public function ensureValidToken(): void
{
if ($this->oauthAccount === null) {
return;
}
$newToken = $this->tokenManager->ensureValidToken($this->oauthAccount);
if ($newToken !== null) {
$this->accessToken = $newToken;
}
}
public function getConfig()
{
return $this->config;
}
// returns only active (archived=false)
public function getOwners(): array
{
return $this->getNewInstance()->crm()->owners()->getAll();
}
/**
* @param bool $archived
*
* @return array<Owner>|[]
*/
public function getOwnersArchived(bool $archived = true): array
{
$endpoint = '/crm/v3/owners';
$queryParams = [
'archived' => $archived ? 'true' : 'false',
];
$queryString = http_build_query($queryParams);
$owners = [];
try {
$response = $this->makeRequest(endpoint: $endpoint, queryString: $queryString);
$responseData = $response?->toArray();
foreach ($responseData['results'] as $result) {
try {
$owners[] = Owner::create($result);
} catch (Throwable $e) {
$this->log->error('[HubSpot] Failed to process owner data', [
'result' => $result,
'error' => $e->getMessage(),
]);
continue;
}
}
} catch (Throwable $e) {
$this->log->error('HubSpot] Failed to fetch owners', [
'archived' => $archived,
'error' => $e->getMessage(),
]);
return [];
}
return $owners;
}
public function getMeeting(string $engagementId): ObjectWithAssociations
{
return $this->getNewInstance()->crm()->objects()->basicApi()
->getById('meeting', $engagementId, null, 'contact,company,deal');
}
public function deleteEngagement(string $engagementId): void
{
$this->getInstance()->engagements()->delete((int) $engagementId);
}
public function getAssociationsData(array $ids, string $fromObject, string $toObject): array
{
$associationData = [];
$idChunks = array_chunk($ids, self::ASSOCIATIONS_BATCH_SIZE_LIMIT);
foreach ($idChunks as $idChunk) {
try {
$batchInput = new \HubSpot\Client\Crm\Associations\Model\BatchInputPublicObjectId();
$batchInput->setInputs(array_map(function ($id) {
$publicObjectId = new \HubSpot\Client\Crm\Associations\Model\PublicObjectId();
$publicObjectId->setId($id);
return $publicObjectId;
}, $idChunk));
$associatedObjectsData = $this
->getNewInstance()
->crm()
->associations()
->batchApi()
->read($fromObject, $toObject, $batchInput);
if ($associatedObjectsData instanceof \HubSpot\Client\Crm\Associations\Model\BatchResponsePublicAssociationMulti) {
foreach ($associatedObjectsData->getResults() as $association) {
$from = $association->getFrom()->getId();
$toAssociations = $association->getTo();
if (! empty($toAssociations)) {
$associationData[$from] = array_map(function ($item) {
return $item->getId();
}, $toAssociations);
}
}
}
} catch (\Exception $e) {
$this->log->error('[Hubspot] Failed to fetch associations', [
'from_object' => $fromObject,
'to_object' => $toObject,
'reason' => $e->getMessage(),
]);
}
}
return $associationData;
}
/**
* @throws \Exception
*/
private function getNoteAssociationType(NoteObject $noteObject): string
{
return match($noteObject) {
NoteObject::Opportunity => 'note_to_deal',
NoteObject::Lead, NoteObject::Contact => 'note_to_contact', // or 'note_to_lead' if your portal supports it
NoteObject::Account => 'note_to_company',
NoteObject::Call, NoteObject::Event => throw new \Exception('Not supported'),
};
}
/**
* @throws \Exception
*/
private function getNoteObject(NoteObject $noteObject): string
{
return match($noteObject) {
NoteObject::Opportunity => 'deal',
NoteObject::Lead, NoteObject::Contact => 'contact',
NoteObject::Account => 'company',
NoteObject::Call, NoteObject::Event => throw new \Exception('Not supported'),
};
}
public function addAssociations(string $objectType, string $associationType, array $payload): Response
{
$endpoint = "/crm/v4/associations/$objectType/$associationType/batch/create";
return $this->makeRequest($endpoint, 'POST', $payload);
}
public function removeAssociations(string $objectType, string $associationType, array $payload): Response
{
$endpoint = "/crm/v4/associations/$objectType/$associationType/batch/archive";
return $this->makeRequest($endpoint, 'POST', $payload);
}
}
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and crm_provider_id is not null order by id desc;\nhttps://app.hubspot.com/contacts/4392066/deal/16964514951/?engagement=96069102624\n https://app.staging.jiminny.com/playback/d5df34dc-bd66-4ff5-a7b3-8d3be30322a0\n\nSELECT * FROM activities WHERE uuid_to_bin('04fdcd0d-818f-4c53-92dc-6f18bc753ffd') = uuid;\n# 609126 softphone tr. 11241\n\nSELECT * FROM activities WHERE uuid_to_bin('6521bfcd-5a30-46e5-9f74-5440fd48befd') = uuid;\n# 608874 conference tr. 11226 crmId: 103422236596\n\nselect * from ai_prompts where transcription_id IN (11241, 11226);\nselect * from activity_summary_logs where activity_id = 608874;\n\nselect * from sidekick_settings;\nselect * from default_activity_types;\n\nselect * from crm_field_data where activity_id = 1223;\n\nselect * from crm_layouts where crm_configuration_id = 2;\nSELECT * FROM crm_layout_entities WHERE crm_layout_id IN (554);\nselect * from crm_fields where crm_configuration_id = 11 and object_type = 'event';\nSELECT * FROM crm_field_values WHERE crm_field_id IN (1455,1450);\n\nSELECT * FROM crm_field_data WHERE crm_layout_entity_id = 971;\nSELECT * FROM crm_field_data WHERE crm_layout_entity_id IN (6494,6495,6496,6497,6498,6499);\n\nSELECT\n CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,\n u.email,\n sa.*,\n t.owner_id FROM social_accounts sa\nJOIN users u\n on u.id = sa.sociable_id\nJOIN teams t on t.id = u.team_id\nWHERE u.team_id = 2 and sa.provider = 'hubspot';\n\nselect * from social_accounts where id = 1499;\n\nselect * from opportunities where team_id = 2\nand crm_provider_id IN ('51317301383');\n\nselect * from contacts where id = 85;\n\nselect * from opportunities where team_id = 2 order by id desc;\nselect * from opportunities where team_id = 2 and crm_provider_id = '51317301383'; # 5112\nselect * from opportunities where team_id = 2 and crm_provider_id = '55976759904'; # 5112\nselect * from opportunity_contacts where opportunity_id = 5117;\nselect * from crm_field_data where object_id = 1365;\nSELECT * FROM crm_fields WHERE id IN (1405, 1407, 1972, 2128);\n\nselect * from features;\nselect * from team_features where team_id IN (1);\nselect * from team_features where feature_id IN (36);\n\nSHOW CREATE TABLE opportunity_contacts;\nSELECT * FROM opportunity_contacts WHERE crm_provider_id = '111751';\n\n# $slug = 'HUBSPOT_WEBHOOK_SYNC';\n# $team = Jiminny\\Models\\Team::find(2);\n# $feature = Feature::query()->where('slug', $slug)->first();\n# TeamFeature::query()->create(['feature_id' => $feature->getId(),'team_id' => $team->getId()]);\n\n# hubspot_webhook_metrics\n\nselect * from opportunities where team_id = 2 and crm_provider_id IN ('374720564','14527423589','49908861993','50435771779'); # 1365\nSELECT * FROM opportunity_contacts WHERE opportunity_id = '414';\nSELECT * FROM opportunity_contacts WHERE crm_provider_id = '131501';\nselect * from contacts where id in (414, 464);\n\nselect * from activities where crm_configuration_id = 2;\n\nselect settings from crm_configurations where id = 11;\n\nselect * from teams; # 1, 2\nselect * from users;\nselect * from crm_configurations where id = 39;\nselect * from team_features where team_id = 2;\nselect * from features;\n# SELECT * FROM opportunities WHERE crm_configuration_id = 2\n# order by id desc;\n# and crm_provider_id = '49908861993';\n\n\nselect * from activity_providers where id IN (443, 202, 203, 227);\n\nselect * from activity_imports where id = 795889;\n\nselect c.id, c.provider, c.settings, t.* from teams t join crm_configurations c on t.id = c.team_id\nwhere c.provider = 'hubspot';\n\nselect * from crm_configurations crm JOIN teams t on crm.team_id = t.id\nwhere provider = 'hubspot';\nSELECT * FROM teams WHERE id = 31;\nSELECT * FROM users WHERE id = 257;\nSELECT * FROM opportunities WHERE team_id = 2;\n\nselect * from opportunity_contacts where opportunity_id = 5124;\nselect * from contacts where id IN (3850,3853,3851,4073,4140,4155,4480,4530,4623,5986,513,687,1806,1523,3613)\n\nselect * from activities where crm_configuration_id = 13;\n\nSELECT * FROM activities WHERE uuid_to_bin('826619ce-ec8e-4e59-8467-a01f5f6ad71e') = uuid; # 418141\n\n\nselect id, team_id, crm_provider_id from crm_configurations where provider = 'hubspot' and crm_provider_id IS NOT NULL;\nSELECT * FROM accounts WHERE team_id = 2 and crm_provider_id = '1212213464' order by id desc;\nSELECT * FROM contacts WHERE team_id = 2 and account_id = 5189 order by id desc;\nSELECT * FROM contacts WHERE team_id = 2 order by id desc;\nselect * from opportunity_contacts where contact_id = 6223;\nSELECT * FROM opportunities WHERE team_id = 2 and account_id = 5189 order by id desc;\n\nselect * from crm_profiles where crm_configuration_id = 2;\n\nselect * from activities where account_id = 46;","depth":4,"on_screen":true,"value":"# **************************** HS **************************************\n\nselect * from teams where id = 2; # 2\nselect * from features; # 2\nselect * from team_features where team_id = 2; # 2\nselect * from crm_configurations where id = 2; # 2\nselect * from users where team_id = 2; #\nselect * from playbooks where team_id = 2; # event 38\nselect * from playbook_categories where playbook_id = 38; #\n\nSELECT * FROM activities WHERE crm_configuration_id = 2 and crm_provider_id is not null order by id desc;\nhttps://app.hubspot.com/contacts/4392066/deal/16964514951/?engagement=96069102624\n https://app.staging.jiminny.com/playback/d5df34dc-bd66-4ff5-a7b3-8d3be30322a0\n\nSELECT * FROM activities WHERE uuid_to_bin('04fdcd0d-818f-4c53-92dc-6f18bc753ffd') = uuid;\n# 609126 softphone tr. 11241\n\nSELECT * FROM activities WHERE uuid_to_bin('6521bfcd-5a30-46e5-9f74-5440fd48befd') = uuid;\n# 608874 conference tr. 11226 crmId: 103422236596\n\nselect * from ai_prompts where transcription_id IN (11241, 11226);\nselect * from activity_summary_logs where activity_id = 608874;\n\nselect * from sidekick_settings;\nselect * from default_activity_types;\n\nselect * from crm_field_data where activity_id = 1223;\n\nselect * from crm_layouts where crm_configuration_id = 2;\nSELECT * FROM crm_layout_entities WHERE crm_layout_id IN (554);\nselect * from crm_fields where crm_configuration_id = 11 and object_type = 'event';\nSELECT * FROM crm_field_values WHERE crm_field_id IN (1455,1450);\n\nSELECT * FROM crm_field_data WHERE crm_layout_entity_id = 971;\nSELECT * FROM crm_field_data WHERE crm_layout_entity_id IN (6494,6495,6496,6497,6498,6499);\n\nSELECT\n CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,\n u.email,\n sa.*,\n t.owner_id FROM social_accounts sa\nJOIN users u\n on u.id = sa.sociable_id\nJOIN teams t on t.id = u.team_id\nWHERE u.team_id = 2 and sa.provider = 'hubspot';\n\nselect * from social_accounts where id = 1499;\n\nselect * from opportunities where team_id = 2\nand crm_provider_id IN ('51317301383');\n\nselect * from contacts where id = 85;\n\nselect * from opportunities where team_id = 2 order by id desc;\nselect * from opportunities where team_id = 2 and crm_provider_id = '51317301383'; # 5112\nselect * from opportunities where team_id = 2 and crm_provider_id = '55976759904'; # 5112\nselect * from opportunity_contacts where opportunity_id = 5117;\nselect * from crm_field_data where object_id = 1365;\nSELECT * FROM crm_fields WHERE id IN (1405, 1407, 1972, 2128);\n\nselect * from features;\nselect * from team_features where team_id IN (1);\nselect * from team_features where feature_id IN (36);\n\nSHOW CREATE TABLE opportunity_contacts;\nSELECT * FROM opportunity_contacts WHERE crm_provider_id = '111751';\n\n# $slug = 'HUBSPOT_WEBHOOK_SYNC';\n# $team = Jiminny\\Models\\Team::find(2);\n# $feature = Feature::query()->where('slug', $slug)->first();\n# TeamFeature::query()->create(['feature_id' => $feature->getId(),'team_id' => $team->getId()]);\n\n# hubspot_webhook_metrics\n\nselect * from opportunities where team_id = 2 and crm_provider_id IN ('374720564','14527423589','49908861993','50435771779'); # 1365\nSELECT * FROM opportunity_contacts WHERE opportunity_id = '414';\nSELECT * FROM opportunity_contacts WHERE crm_provider_id = '131501';\nselect * from contacts where id in (414, 464);\n\nselect * from activities where crm_configuration_id = 2;\n\nselect settings from crm_configurations where id = 11;\n\nselect * from teams; # 1, 2\nselect * from users;\nselect * from crm_configurations where id = 39;\nselect * from team_features where team_id = 2;\nselect * from features;\n# SELECT * FROM opportunities WHERE crm_configuration_id = 2\n# order by id desc;\n# and crm_provider_id = '49908861993';\n\n\nselect * from activity_providers where id IN (443, 202, 203, 227);\n\nselect * from activity_imports where id = 795889;\n\nselect c.id, c.provider, c.settings, t.* from teams t join crm_configurations c on t.id = c.team_id\nwhere c.provider = 'hubspot';\n\nselect * from crm_configurations crm JOIN teams t on crm.team_id = t.id\nwhere provider = 'hubspot';\nSELECT * FROM teams WHERE id = 31;\nSELECT * FROM users WHERE id = 257;\nSELECT * FROM opportunities WHERE team_id = 2;\n\nselect * from opportunity_contacts where opportunity_id = 5124;\nselect * from contacts where id IN (3850,3853,3851,4073,4140,4155,4480,4530,4623,5986,513,687,1806,1523,3613)\n\nselect * from activities where crm_configuration_id = 13;\n\nSELECT * FROM activities WHERE uuid_to_bin('826619ce-ec8e-4e59-8467-a01f5f6ad71e') = uuid; # 418141\n\n\nselect id, team_id, crm_provider_id from crm_configurations where provider = 'hubspot' and crm_provider_id IS NOT NULL;\nSELECT * FROM accounts WHERE team_id = 2 and crm_provider_id = '1212213464' order by id desc;\nSELECT * FROM contacts WHERE team_id = 2 and account_id = 5189 order by id desc;\nSELECT * FROM contacts WHERE team_id = 2 order by id desc;\nselect * from opportunity_contacts where contact_id = 6223;\nSELECT * FROM opportunities WHERE team_id = 2 and account_id = 5189 order by id desc;\n\nselect * from crm_profiles where crm_configuration_id = 2;\n\nselect * from activities where account_id = 46;","role_description":"text entry area","is_enabled":true,"is_focused":true,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Sync Changes","depth":4,"bounds":{"left":0.27027926,"top":1.0,"width":0.008643617,"height":0.0},"on_screen":false,"role_description":"button","is_enabled":false,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Hide This Notification","depth":4,"bounds":{"left":0.27027926,"top":1.0,"width":0.008643617,"height":0.0},"on_screen":false,"role_description":"button","is_enabled":false,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Code changed:","depth":4,"bounds":{"left":0.27027926,"top":1.0,"width":0.042220745,"height":0.0},"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Hide","depth":4,"bounds":{"left":0.27027926,"top":1.0,"width":0.008643617,"height":0.0},"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"2","depth":4,"bounds":{"left":0.27027926,"top":1.0,"width":0.007978723,"height":0.0},"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"60","depth":4,"bounds":{"left":0.27027926,"top":1.0,"width":0.010305851,"height":0.0},"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Previous Highlighted Error","depth":4,"bounds":{"left":0.27027926,"top":1.0,"width":0.00731383,"height":0.0},"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Next Highlighted Error","depth":4,"bounds":{"left":0.27027926,"top":1.0,"width":0.006981383,"height":0.0},"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXTextArea","text":"<?php\n\ndeclare(strict_types=1);\n\nnamespace Jiminny\\Services\\Crm\\Hubspot;\n\nuse HubSpot\\Client\\Crm\\Deals\\ApiException as DealApiException;\nuse HubSpot\\Client\\Crm\\Contacts\\ApiException as ContactApiException;\nuse HubSpot\\Client\\Crm\\Companies\\ApiException as CompanyApiException;\nuse HubSpot\\Client\\Crm\\Contacts\\Model\\SimplePublicObjectWithAssociations as ContactsWithAssociations;\nuse HubSpot\\Client\\Crm\\Companies\\Model\\SimplePublicObjectWithAssociations as CompaniesWithAssociations;\nuse HubSpot\\Client\\Crm\\Deals\\Model\\SimplePublicObjectWithAssociations as DealWithAssociations;\nuse HubSpot\\Client\\Crm\\Objects\\Model\\SimplePublicObjectInput;\nuse HubSpot\\Client\\Crm\\Objects\\Model\\SimplePublicObjectWithAssociations as ObjectWithAssociations;\nuse HubSpot\\Client\\Crm\\Pipelines\\Model\\Error;\nuse HubSpot\\Client\\Crm\\Pipelines\\Model\\PipelineStage;\nuse HubSpot\\Client\\Crm\\Properties\\Model\\Property;\nuse HubSpot\\Discovery\\Discovery;\nuse Jiminny\\Component\\Utility\\Service\\ProviderRateLimiter;\nuse Jiminny\\Exceptions\\CrmException;\nuse Jiminny\\Exceptions\\RateLimitException;\nuse Jiminny\\Exceptions\\SocialAccountTokenInvalidException;\nuse Jiminny\\Jobs\\Crm\\NoteObject;\nuse Jiminny\\Models\\Crm\\Field;\nuse Jiminny\\Services\\Crm\\BaseClient;\nuse Jiminny\\Services\\Crm\\Hubspot\\DTO\\Response\\Owner;\nuse Jiminny\\Services\\SocialAccountService;\nuse SevenShores\\Hubspot\\Exceptions\\BadRequest;\nuse SevenShores\\Hubspot\\Exceptions\\HubspotException;\nuse SevenShores\\Hubspot\\Factory;\nuse SevenShores\\Hubspot\\Http\\Response;\nuse Jiminny\\Services\\Crm\\Hubspot\\Pagination\\HubspotPaginationService;\nuse Throwable;\n\n/**\n * @phpstan-type CrmFieldOption array{id:string, label:string, value?:string}\n */\nclass Client extends BaseClient implements HubspotClientInterface\n{\n public const string MIN_API_VERSION = '2';\n\n public const string BASE_URL = 'https://api.hubapi.com';\n\n public const int ASSOCIATIONS_BATCH_SIZE_LIMIT = 1000;\n\n private HubspotPaginationService $paginationService;\n private HubspotTokenManager $tokenManager;\n private ProviderRateLimiter $rateLimiter;\n\n public function __construct(\n SocialAccountService $socialAccountService,\n HubspotPaginationService $paginationService,\n HubspotTokenManager $tokenManager,\n ProviderRateLimiter $rateLimiter,\n ) {\n parent::__construct($socialAccountService);\n $this->paginationService = $paginationService;\n $this->tokenManager = $tokenManager;\n $this->rateLimiter = $rateLimiter;\n\n $this->setBaseUrl(self::BASE_URL);\n $this->setVersion(self::MIN_API_VERSION);\n }\n\n /**\n * Single entry point for every HubSpot API call. Enforces the per-portal\n * rate limit configured in the rate_limits table (morphed to the current\n * Configuration) and reacts to a real 429 from HubSpot by translating it\n * into a RateLimitException carrying Retry-After.\n *\n * Wrap any outbound HubSpot call (SDK or raw HTTP) like:\n *\n * $this->executeRequest(fn () => $this->getNewInstance()->crm()->...);\n *\n * @template T\n * @param callable(): T $apiCall\n * @return T\n *\n * @throws RateLimitException\n */\n private function executeRequest(callable $apiCall)\n {\n if (! $this->rateLimiter->canMakeRequest($this->config)) {\n $retryAfter = $this->rateLimiter->requestAvailableIn($this->config);\n\n $this->log->warning('[Hubspot] Rate limit exceeded, deferring request', [\n 'team_id' => $this->config->team_id,\n 'config_id' => $this->config->getId(),\n 'retry_after' => $retryAfter,\n ]);\n\n throw new RateLimitException(\n 'Hubspot rate limit reached for configuration ' . $this->config->getId(),\n $retryAfter,\n );\n }\n\n $this->rateLimiter->incrementRequestCount($this->config);\n\n try {\n return $apiCall();\n } catch (Throwable $e) {\n if ($this->isHubspotRateLimit($e)) {\n $retryAfter = $this->parseRetryAfter($e);\n\n $this->log->warning('[Hubspot] Received 429 from API', [\n 'team_id' => $this->config->team_id,\n 'config_id' => $this->config->getId(),\n 'retry_after' => $retryAfter,\n 'reason' => $e->getMessage(),\n ]);\n\n throw new RateLimitException('Hubspot returned 429', $retryAfter, $e);\n }\n\n throw $e;\n }\n }\n\n private function isHubspotRateLimit(Throwable $e): bool\n {\n return method_exists($e, 'getCode') && (int) $e->getCode() === 429;\n }\n\n private function parseRetryAfter(Throwable $e): int\n {\n if (method_exists($e, 'getResponseHeaders')) {\n $headers = $e->getResponseHeaders() ?: [];\n $value = $headers['Retry-After'] ?? $headers['retry-after'] ?? null;\n if (is_array($value)) {\n $value = $value[0] ?? null;\n }\n if (is_numeric($value)) {\n return (int) $value;\n }\n }\n\n return 10;\n }\n\n public function getMinimumApiVersion(): string\n {\n return self::MIN_API_VERSION;\n }\n\n public function getInstance(): Factory\n {\n return new Factory([\n 'key' => $this->accessToken,\n 'oauth2' => true,\n 'base_url' => $this->baseUrl,\n ]);\n }\n\n public function getNewInstance(): Discovery\n {\n return \\HubSpot\\Factory::createWithAccessToken($this->accessToken);\n }\n\n /**\n * Secondly and daily limits for Hubspot API\n *\n * Product Tier: Free & Starter | Professional & Enterprise | API add-on (any tier)\n * Burst: 100/10 seconds | 150/10 seconds | 200/10 seconds\n * Daily: 250,000 | 500,000 | 1,000,000\n *\n * Official documentation states: The search endpoints are rate limited to five requests per second.\n * Since with 5 RPS were still hitting secondly rate limits we lowered it to 4\n */\n public function getPaginatedData(array $payload, string $type, int $offset = 0): array\n {\n $total = 0;\n $lastId = null;\n $rows = [];\n foreach ($this->getPaginatedDataGenerator($payload, $type, $offset, $total, $lastId) as $row) {\n $rows[] = $row;\n }\n\n return ['results' => $rows, 'total' => $total, 'last_record' => $lastId];\n }\n\n /**\n * @throws HubspotException\n * @throws SocialAccountTokenInvalidException\n * @throws BadRequest\n */\n public function getPaginatedDataGenerator(\n array $payload,\n string $type,\n int $offset = 0,\n int &$total = 0,\n ?string &$lastRecordId = null\n ): \\Generator {\n return $this->paginationService->getPaginatedDataGenerator(\n $this,\n $payload,\n $type,\n $offset,\n $total,\n $lastRecordId\n );\n }\n\n /**\n * @throws DealApiException\n * @throws CrmException\n */\n public function getOpportunityById(string $crmId, array $fields): array\n {\n try {\n $deal = $this->executeRequest(fn () => $this->getNewInstance()->crm()->deals()->basicApi()->getById(\n $crmId,\n implode(',', $fields),\n 'companies,contacts'\n ));\n } catch (DealApiException $e) {\n $this->log->info('[Hubspot] Failed to fetch opportunity', [\n 'crm_id' => $crmId,\n 'reason' => $e->getMessage(),\n ]);\n\n throw $e;\n }\n\n if (! $deal instanceof DealWithAssociations) {\n throw new CrmException('Deal not found');\n }\n\n return [\n 'id' => $deal->getId(),\n 'properties' => $deal->getProperties(),\n 'associations' => $deal->getAssociations(),\n ];\n }\n\n /**\n * Generic batch read method for HubSpot objects\n *\n * @param string $objectType The object type ('deals', 'companies', 'contacts')\n * @param array<string> $crmIds Array of HubSpot object IDs (max 100)\n * @param array<string> $fields Array of property names to fetch\n *\n * @return array<string, array> Array keyed by CRM ID with object data\n */\n private function batchReadObjects(string $objectType, array $crmIds, array $fields): array\n {\n if (empty($crmIds)) {\n return [];\n }\n\n $this->validateBatchSize($objectType, $crmIds);\n $this->ensureValidToken();\n\n try {\n $batchConfig = $this->createBatchConfiguration($objectType);\n $batchReadRequest = $this->prepareBatchRequest($batchConfig, $crmIds, $fields);\n $response = $batchConfig['api']->read($batchReadRequest);\n\n $this->validateApiResponse($response, $objectType);\n\n $results = $this->processApiResults($response);\n $this->logBatchResults($objectType, $crmIds, $results);\n\n return $results;\n } catch (\\Throwable $e) {\n $this->handleBatchError($e, $objectType, $crmIds);\n }\n }\n\n private function validateBatchSize(string $objectType, array $crmIds): void\n {\n if (count($crmIds) > 100) {\n throw new \\InvalidArgumentException(\"Batch size cannot exceed 100 {$objectType}\");\n }\n }\n\n private function createBatchConfiguration(string $objectType): array\n {\n $configurations = [\n 'deals' => [\n 'batchReadRequest' => new \\HubSpot\\Client\\Crm\\Deals\\Model\\BatchReadInputSimplePublicObjectId(),\n 'inputClass' => \\HubSpot\\Client\\Crm\\Deals\\Model\\SimplePublicObjectId::class,\n 'api' => $this->getNewInstance()->crm()->deals()->batchApi(),\n ],\n 'companies' => [\n 'batchReadRequest' => new \\HubSpot\\Client\\Crm\\Companies\\Model\\BatchReadInputSimplePublicObjectId(),\n 'inputClass' => \\HubSpot\\Client\\Crm\\Companies\\Model\\SimplePublicObjectId::class,\n 'api' => $this->getNewInstance()->crm()->companies()->batchApi(),\n ],\n 'contacts' => [\n 'batchReadRequest' => new \\HubSpot\\Client\\Crm\\Contacts\\Model\\BatchReadInputSimplePublicObjectId(),\n 'inputClass' => \\HubSpot\\Client\\Crm\\Contacts\\Model\\SimplePublicObjectId::class,\n 'api' => $this->getNewInstance()->crm()->contacts()->batchApi(),\n ],\n ];\n\n if (! isset($configurations[$objectType])) {\n throw new \\InvalidArgumentException(\"Unsupported object type: {$objectType}\");\n }\n\n return $configurations[$objectType];\n }\n\n private function prepareBatchRequest(array $batchConfig, array $crmIds, array $fields): object\n {\n $batchReadRequest = $batchConfig['batchReadRequest'];\n $inputClass = $batchConfig['inputClass'];\n\n $inputs = array_map(function ($crmId) use ($inputClass) {\n $input = new $inputClass();\n $input->setId($crmId);\n\n return $input;\n }, $crmIds);\n\n $batchReadRequest->setInputs($inputs);\n $batchReadRequest->setProperties($fields);\n\n return $batchReadRequest;\n }\n\n private function validateApiResponse($response, string $objectType): void\n {\n if (! $response) {\n throw new CrmException(\"HubSpot API returned null response for {$objectType} batch read\");\n }\n }\n\n private function processApiResults($response): array\n {\n $results = [];\n $responseResults = $response->getResults();\n\n if ($responseResults) {\n foreach ($responseResults as $object) {\n if ($object && $object->getId()) {\n $results[$object->getId()] = [\n 'id' => $object->getId(),\n 'properties' => $object->getProperties() ?: [],\n ];\n }\n }\n }\n\n return $results;\n }\n\n private function logBatchResults(string $objectType, array $crmIds, array $results): void\n {\n $this->log->info(\"[HubSpot] Batch fetched {$objectType}\", [\n 'requested_count' => count($crmIds),\n 'returned_count' => count($results),\n 'crm_ids' => $crmIds,\n ]);\n }\n\n private function handleBatchError(\\Throwable $e, string $objectType, array $crmIds): void\n {\n $errorMessage = $e->getMessage() ?: 'Unknown error';\n $errorTrace = $e->getTraceAsString() ?: 'No trace available';\n\n $this->log->error(\"[HubSpot] Failed to batch fetch {$objectType}\", [\n 'crm_ids' => $crmIds,\n 'error' => $errorMessage,\n 'trace' => $errorTrace,\n ]);\n\n throw new CrmException(\"Failed to batch fetch {$objectType}: \" . $errorMessage);\n }\n\n /**\n * Batch read multiple opportunities by their CRM IDs\n *\n * @param array<string> $crmIds Array of HubSpot deal IDs (max 100)\n * @param array<string> $fields Array of property names to fetch\n *\n * @return array<string, array> Array keyed by CRM ID with opportunity data\n */\n public function getOpportunitiesByIds(array $crmIds, array $fields): array\n {\n return $this->batchReadObjects('deals', $crmIds, $fields);\n }\n\n /**\n * Batch read multiple companies by their CRM IDs\n *\n * @param array<string> $crmIds Array of HubSpot company IDs (max 100)\n * @param array<string> $fields Array of property names to fetch\n *\n * @return array<string, array> Array keyed by CRM ID with company data\n */\n public function getCompaniesByIds(array $crmIds, array $fields): array\n {\n return $this->batchReadObjects('companies', $crmIds, $fields);\n }\n\n /**\n * Batch read multiple contacts by their CRM IDs\n *\n * @param array<string> $crmIds Array of HubSpot contact IDs (max 100)\n * @param array<string> $fields Array of property names to fetch\n *\n * @return array<string, array> Array keyed by CRM ID with contact data\n */\n public function getContactsByIds(array $crmIds, array $fields): array\n {\n return $this->batchReadObjects('contacts', $crmIds, $fields);\n }\n\n /**\n * @throws CompanyApiException\n * @throws CrmException\n */\n public function getAccountById(string $crmId, array $fields): array\n {\n try {\n $company = $this->getNewInstance()->crm()->companies()->basicApi()->getById(\n $crmId,\n implode(',', $fields),\n );\n } catch (CompanyApiException $e) {\n $this->log->info('[Hubspot] Failed to fetch account', [\n 'crm_id' => $crmId,\n 'reason' => $e->getMessage(),\n ]);\n\n throw $e;\n }\n\n if (! $company instanceof CompaniesWithAssociations) {\n throw new CrmException('Account not found');\n }\n\n return [\n 'id' => $company->getId(),\n 'properties' => $company->getProperties(),\n ];\n }\n\n /**\n * @throws ContactApiException\n * @throws CrmException\n */\n public function getContactById(string $crmId, array $fields): array\n {\n try {\n $contact = $this->getNewInstance()->crm()->contacts()->basicApi()->getById(\n $crmId,\n implode(',', $fields)\n );\n } catch (ContactApiException $e) {\n $this->log->info('[Hubspot] Failed to fetch contact', [\n 'crm_id' => $crmId,\n 'reason' => $e->getMessage(),\n ]);\n\n throw $e;\n }\n\n if (! $contact instanceof ContactsWithAssociations) {\n throw new CrmException('Contact not found');\n }\n\n return [\n 'id' => $contact->getId(),\n 'properties' => $contact->getProperties(),\n ];\n }\n\n /**\n * This is email search request that Hubspot offers as GET (more generous quota)\n */\n public function getContactByEmail(string $email, array $fields = []): array\n {\n try {\n $contact = $this->getNewInstance()->crm()->contacts()->basicApi()->getById(\n $email,\n implode(',', $fields),\n null,\n false,\n 'email'\n );\n\n return [\n 'id' => $contact->getId(),\n 'properties' => $contact->getProperties(),\n ];\n } catch (ContactApiException $e) {\n $this->log->info('[Hubspot] Failed to fetch contact', [\n 'email' => $email,\n 'reason' => $e->getMessage(),\n ]);\n\n return [];\n }\n }\n\n /**\n * @throws CrmException\n */\n public function fetchProperty(string $objectType, string $propertyId): Property\n {\n $result = $this->getNewInstance()->crm()->properties()->coreApi()->getByName($objectType, $propertyId);\n\n if (! $result instanceof Property) {\n $this->log->error('[Hubspot] Failed to fetch property', [\n 'object_type' => $objectType,\n 'property_id' => $propertyId,\n 'reason' => $result->getMessage(),\n ]);\n\n throw new CrmException('Failed to fetch property');\n }\n\n return $result;\n }\n\n /**\n * @return array<CrmFieldOption>\n */\n public function fetchPropertyOptions(string $objectType, string $propertyId): array\n {\n /** @var array<CrmFieldOption> */\n return $this->fetchProperty($objectType, $propertyId)->getOptions();\n }\n\n /**\n * @return array<array{id:string, label:string, deleted:bool}>\n */\n public function fetchCallDispositions(): array\n {\n /** @var Response $response */\n $response = $this->getInstance()->engagements()->getCallDispositions();\n\n /**\n * @var array<array{\n * id:string,\n * label:string,\n * deleted: bool\n * }>\n */\n return $response->toArray();\n }\n\n /**\n * @return array<CrmFieldOption>\n */\n public function fetchOpportunityPipelineStages(): array\n {\n $stages = [];\n $apiResponse = $this->getNewInstance()->crm()->pipelines()->pipelinesApi()->getAll('deals');\n\n if ($apiResponse instanceof Error) {\n $this->log->error('[Hubspot] Failed to fetch opportunity pipelines', [\n 'reason' => $apiResponse->getMessage(),\n ]);\n\n return [];\n }\n\n foreach ($apiResponse->getResults() as $pipeline) {\n $pipelineStages = array_map(\n static function (PipelineStage $stage) {\n return [\n 'id' => $stage->getId(),\n 'label' => $stage->getLabel(),\n ];\n },\n $pipeline->getStages()\n );\n\n $stages = array_merge($stages, $pipelineStages);\n }\n\n return $stages;\n }\n\n public function fetchOpportunityPipelines(): array\n {\n $pipelines = [];\n\n try {\n $apiResponse = $this->makeRequest('/crm/v3/pipelines/deals');\n } catch (\\Exception $e) {\n $this->log->info('[Hubspot] Failed to fetch opportunity pipelines', [\n 'reason' => $e->getMessage(),\n ]);\n\n return [];\n }\n\n $response = $apiResponse->toArray();\n\n foreach ($response['results'] as $pipeline) {\n $pipelines[] = [\n 'id' => $pipeline['id'],\n 'label' => $pipeline['label'],\n ];\n }\n\n return $pipelines;\n }\n\n /**\n * @return array<CrmFieldOption>\n */\n public function fetchMeetingOutcomeFieldOptions(Field $field): array\n {\n return $field->getCrmProviderId() === 'meetingOutcome'\n ? $this->fetchMeetingOutcomeTypes()\n : $this->fetchCallActivityTypes();\n }\n\n public function fetchMeetingOutcomeTypes(): array\n {\n return $this->extractMeetingTypeOptions(\n 'https://api.hubapi.com/crm/v3/properties/meeting/hs_meeting_outcome'\n );\n }\n\n public function fetchCallActivityTypes(): array\n {\n return $this->extractMeetingTypeOptions(\n 'https://api.hubapi.com/crm/v3/properties/call/hs_activity_type'\n );\n }\n\n private function extractMeetingTypeOptions(string $endpoint): array\n {\n /** @var Response $response */\n $response = $this->getInstance()\n ->getClient()\n ->request('GET', $endpoint);\n\n /**\n * @var array<array{\n * value: string,\n * label: string,\n * displayOrder: int\n * }> $optionData\n */\n $optionData = $response->toArray()['options'] ?? [];\n\n $options = [];\n foreach ($optionData as $item) {\n $options[] = [\n 'id' => $item['value'],\n 'value' => $item['value'],\n 'label' => $item['label'],\n 'display_order' => $item['displayOrder'],\n ];\n }\n\n return $options;\n }\n\n /**\n * @return array<CrmFieldOption>\n */\n public function fetchDispositionFieldOptions(): array\n {\n $options = [];\n\n $dispositions = $this->fetchCallDispositions();\n\n foreach ($dispositions as $disposition) {\n if ($disposition['deleted'] !== false) {\n continue;\n }\n\n $option['value'] = $disposition['id'];\n $option['id'] = $disposition['id'];\n $option['label'] = $disposition['label'];\n\n $options[] = $option;\n }\n\n return $options;\n }\n\n /**\n * @return array<CrmFieldOption>\n */\n public function fetchOpportunityFieldOptions(Field $field): array\n {\n if ($field->isStageField()) {\n return $this->fetchOpportunityPipelineStages();\n }\n\n if ($field->isPipelineField()) {\n return $this->fetchOpportunityPipelines();\n }\n\n return $this->fetchPropertyOptions('deals', $field->getCrmProviderId());\n }\n\n /**\n * @throws BadRequest\n * @throws HubspotException\n */\n public function makeRequest(string $endpoint, $method = 'GET', $payload = [], ?string $queryString = null)\n {\n $endpoint = self::BASE_URL . $endpoint;\n\n if ($method === 'GET') {\n return $this->getInstance()->getClient()?->request(\n method: $method,\n endpoint: $endpoint,\n query_string: $queryString\n );\n } else {\n return $this->getInstance()->getClient()->request($method, $endpoint, [\n 'json' => ($payload),\n ]);\n }\n }\n\n /**\n * @throws BadRequest\n * @throws HubspotException\n */\n public function createMeeting(array $payload): Response\n {\n $endpoint = '/crm/v3/objects/meetings';\n\n return $this->makeRequest($endpoint, 'POST', $payload);\n }\n\n /**\n * @throws BadRequest\n * @throws HubspotException\n */\n public function updateMeeting(string $meetingId, array $payload): Response\n {\n $endpoint = '/crm/v3/objects/meetings/' . $meetingId;\n\n return $this->makeRequest($endpoint, 'PATCH', $payload);\n }\n\n /**\n * @throws \\Exception\n */\n public function createNote(\n string $body,\n string $ownerId,\n int $timestamp,\n string $objectId,\n NoteObject $noteObject\n ): ?string {\n try {\n $noteInput = new SimplePublicObjectInput([\n 'properties' => [\n 'hs_note_body' => $body,\n 'hubspot_owner_id' => $ownerId,\n 'hs_timestamp' => $timestamp,\n ],\n ]);\n\n // Create note\n $note = $this->getNewInstance()->crm()->objects()->basicApi()->create('note', $noteInput);\n\n $this->getNewInstance()->crm()->objects()->associationsApi()->create(\n 'note',\n $note->getId(),\n $this->getNoteObject($noteObject),\n $objectId,\n $this->getNoteAssociationType($noteObject),\n );\n\n return $note->getId();\n } catch (\\Exception $e) {\n $this->log->error('[Hubspot] Failed to create note', [\n 'objectId' => $objectId,\n 'noteObject' => $noteObject->getObjectType(),\n 'reason' => $e->getMessage(),\n ]);\n\n \\Sentry::captureException($e);\n }\n\n return null;\n }\n\n public function updateEngagement(string $objectId, array $engagement, array $metadata): void\n {\n $this->getInstance()->engagements()->update($objectId, $engagement, $metadata);\n }\n\n public function getEngagementData(string $engagementId): array\n {\n $engagement = $this->getInstance()->engagements()->get($engagementId);\n\n return $engagement->toArray();\n }\n\n public function createEngagement(array $engagement, array $associations, array $metadata): Response\n {\n return $this->getInstance()\n ->engagements()\n ->create($engagement, $associations, $metadata);\n }\n\n public function isUnauthorizedException(\\Exception $e): bool\n {\n // Check for specific HubSpot API exception types first\n if ($e instanceof BadRequest) {\n // BadRequest can contain 401 status codes\n return $e->getCode() === 401;\n }\n\n // Check for HTTP client exceptions with status codes\n if ($e instanceof \\GuzzleHttp\\Exception\\RequestException && $e->hasResponse()) {\n $response = $e->getResponse();\n if ($response !== null) {\n return $response->getStatusCode() === 401;\n }\n }\n\n // Check for Guzzle HTTP exceptions\n if ($e instanceof \\GuzzleHttp\\Exception\\ClientException) {\n return $e->getCode() === 401;\n }\n\n // Fallback to string matching as last resort, but be more specific\n $message = strtolower($e->getMessage());\n\n return str_contains($message, '401 unauthorized') ||\n str_contains($message, 'http 401') ||\n str_contains($message, 'status code 401') ||\n (preg_match('/\\b401\\b/', $message) && str_contains($message, 'unauthorized'));\n }\n\n /**\n * Validates and refreshes the access token if needed before API requests.\n * This ensures long-running processes don't fail due to token expiration.\n *\n * @throws SocialAccountTokenInvalidException\n */\n public function ensureValidToken(): void\n {\n if ($this->oauthAccount === null) {\n return;\n }\n\n $newToken = $this->tokenManager->ensureValidToken($this->oauthAccount);\n if ($newToken !== null) {\n $this->accessToken = $newToken;\n }\n }\n\n public function getConfig()\n {\n return $this->config;\n }\n\n // returns only active (archived=false)\n public function getOwners(): array\n {\n return $this->getNewInstance()->crm()->owners()->getAll();\n }\n\n /**\n * @param bool $archived\n *\n * @return array<Owner>|[]\n */\n public function getOwnersArchived(bool $archived = true): array\n {\n $endpoint = '/crm/v3/owners';\n $queryParams = [\n 'archived' => $archived ? 'true' : 'false',\n ];\n $queryString = http_build_query($queryParams);\n\n $owners = [];\n\n try {\n $response = $this->makeRequest(endpoint: $endpoint, queryString: $queryString);\n $responseData = $response?->toArray();\n\n foreach ($responseData['results'] as $result) {\n try {\n $owners[] = Owner::create($result);\n } catch (Throwable $e) {\n $this->log->error('[HubSpot] Failed to process owner data', [\n 'result' => $result,\n 'error' => $e->getMessage(),\n ]);\n\n continue;\n }\n }\n } catch (Throwable $e) {\n $this->log->error('HubSpot] Failed to fetch owners', [\n 'archived' => $archived,\n 'error' => $e->getMessage(),\n ]);\n\n return [];\n }\n\n return $owners;\n }\n\n public function getMeeting(string $engagementId): ObjectWithAssociations\n {\n return $this->getNewInstance()->crm()->objects()->basicApi()\n ->getById('meeting', $engagementId, null, 'contact,company,deal');\n }\n\n public function deleteEngagement(string $engagementId): void\n {\n $this->getInstance()->engagements()->delete((int) $engagementId);\n }\n\n public function getAssociationsData(array $ids, string $fromObject, string $toObject): array\n {\n $associationData = [];\n $idChunks = array_chunk($ids, self::ASSOCIATIONS_BATCH_SIZE_LIMIT);\n\n foreach ($idChunks as $idChunk) {\n try {\n $batchInput = new \\HubSpot\\Client\\Crm\\Associations\\Model\\BatchInputPublicObjectId();\n $batchInput->setInputs(array_map(function ($id) {\n $publicObjectId = new \\HubSpot\\Client\\Crm\\Associations\\Model\\PublicObjectId();\n $publicObjectId->setId($id);\n\n return $publicObjectId;\n }, $idChunk));\n\n $associatedObjectsData = $this\n ->getNewInstance()\n ->crm()\n ->associations()\n ->batchApi()\n ->read($fromObject, $toObject, $batchInput);\n\n if ($associatedObjectsData instanceof \\HubSpot\\Client\\Crm\\Associations\\Model\\BatchResponsePublicAssociationMulti) {\n foreach ($associatedObjectsData->getResults() as $association) {\n $from = $association->getFrom()->getId();\n $toAssociations = $association->getTo();\n\n if (! empty($toAssociations)) {\n $associationData[$from] = array_map(function ($item) {\n return $item->getId();\n }, $toAssociations);\n }\n }\n }\n } catch (\\Exception $e) {\n $this->log->error('[Hubspot] Failed to fetch associations', [\n 'from_object' => $fromObject,\n 'to_object' => $toObject,\n 'reason' => $e->getMessage(),\n ]);\n }\n }\n\n return $associationData;\n }\n\n /**\n * @throws \\Exception\n */\n private function getNoteAssociationType(NoteObject $noteObject): string\n {\n return match($noteObject) {\n NoteObject::Opportunity => 'note_to_deal',\n NoteObject::Lead, NoteObject::Contact => 'note_to_contact', // or 'note_to_lead' if your portal supports it\n NoteObject::Account => 'note_to_company',\n NoteObject::Call, NoteObject::Event => throw new \\Exception('Not supported'),\n };\n }\n\n /**\n * @throws \\Exception\n */\n private function getNoteObject(NoteObject $noteObject): string\n {\n return match($noteObject) {\n NoteObject::Opportunity => 'deal',\n NoteObject::Lead, NoteObject::Contact => 'contact',\n NoteObject::Account => 'company',\n NoteObject::Call, NoteObject::Event => throw new \\Exception('Not supported'),\n };\n }\n\n public function addAssociations(string $objectType, string $associationType, array $payload): Response\n {\n $endpoint = \"/crm/v4/associations/$objectType/$associationType/batch/create\";\n\n return $this->makeRequest($endpoint, 'POST', $payload);\n }\n\n public function removeAssociations(string $objectType, string $associationType, array $payload): Response\n {\n $endpoint = \"/crm/v4/associations/$objectType/$associationType/batch/archive\";\n\n return $this->makeRequest($endpoint, 'POST', $payload);\n }\n}","depth":4,"on_screen":true,"value":"<?php\n\ndeclare(strict_types=1);\n\nnamespace Jiminny\\Services\\Crm\\Hubspot;\n\nuse HubSpot\\Client\\Crm\\Deals\\ApiException as DealApiException;\nuse HubSpot\\Client\\Crm\\Contacts\\ApiException as ContactApiException;\nuse HubSpot\\Client\\Crm\\Companies\\ApiException as CompanyApiException;\nuse HubSpot\\Client\\Crm\\Contacts\\Model\\SimplePublicObjectWithAssociations as ContactsWithAssociations;\nuse HubSpot\\Client\\Crm\\Companies\\Model\\SimplePublicObjectWithAssociations as CompaniesWithAssociations;\nuse HubSpot\\Client\\Crm\\Deals\\Model\\SimplePublicObjectWithAssociations as DealWithAssociations;\nuse HubSpot\\Client\\Crm\\Objects\\Model\\SimplePublicObjectInput;\nuse HubSpot\\Client\\Crm\\Objects\\Model\\SimplePublicObjectWithAssociations as ObjectWithAssociations;\nuse HubSpot\\Client\\Crm\\Pipelines\\Model\\Error;\nuse HubSpot\\Client\\Crm\\Pipelines\\Model\\PipelineStage;\nuse HubSpot\\Client\\Crm\\Properties\\Model\\Property;\nuse HubSpot\\Discovery\\Discovery;\nuse Jiminny\\Component\\Utility\\Service\\ProviderRateLimiter;\nuse Jiminny\\Exceptions\\CrmException;\nuse Jiminny\\Exceptions\\RateLimitException;\nuse Jiminny\\Exceptions\\SocialAccountTokenInvalidException;\nuse Jiminny\\Jobs\\Crm\\NoteObject;\nuse Jiminny\\Models\\Crm\\Field;\nuse Jiminny\\Services\\Crm\\BaseClient;\nuse Jiminny\\Services\\Crm\\Hubspot\\DTO\\Response\\Owner;\nuse Jiminny\\Services\\SocialAccountService;\nuse SevenShores\\Hubspot\\Exceptions\\BadRequest;\nuse SevenShores\\Hubspot\\Exceptions\\HubspotException;\nuse SevenShores\\Hubspot\\Factory;\nuse SevenShores\\Hubspot\\Http\\Response;\nuse Jiminny\\Services\\Crm\\Hubspot\\Pagination\\HubspotPaginationService;\nuse Throwable;\n\n/**\n * @phpstan-type CrmFieldOption array{id:string, label:string, value?:string}\n */\nclass Client extends BaseClient implements HubspotClientInterface\n{\n public const string MIN_API_VERSION = '2';\n\n public const string BASE_URL = 'https://api.hubapi.com';\n\n public const int ASSOCIATIONS_BATCH_SIZE_LIMIT = 1000;\n\n private HubspotPaginationService $paginationService;\n private HubspotTokenManager $tokenManager;\n private ProviderRateLimiter $rateLimiter;\n\n public function __construct(\n SocialAccountService $socialAccountService,\n HubspotPaginationService $paginationService,\n HubspotTokenManager $tokenManager,\n ProviderRateLimiter $rateLimiter,\n ) {\n parent::__construct($socialAccountService);\n $this->paginationService = $paginationService;\n $this->tokenManager = $tokenManager;\n $this->rateLimiter = $rateLimiter;\n\n $this->setBaseUrl(self::BASE_URL);\n $this->setVersion(self::MIN_API_VERSION);\n }\n\n /**\n * Single entry point for every HubSpot API call. Enforces the per-portal\n * rate limit configured in the rate_limits table (morphed to the current\n * Configuration) and reacts to a real 429 from HubSpot by translating it\n * into a RateLimitException carrying Retry-After.\n *\n * Wrap any outbound HubSpot call (SDK or raw HTTP) like:\n *\n * $this->executeRequest(fn () => $this->getNewInstance()->crm()->...);\n *\n * @template T\n * @param callable(): T $apiCall\n * @return T\n *\n * @throws RateLimitException\n */\n private function executeRequest(callable $apiCall)\n {\n if (! $this->rateLimiter->canMakeRequest($this->config)) {\n $retryAfter = $this->rateLimiter->requestAvailableIn($this->config);\n\n $this->log->warning('[Hubspot] Rate limit exceeded, deferring request', [\n 'team_id' => $this->config->team_id,\n 'config_id' => $this->config->getId(),\n 'retry_after' => $retryAfter,\n ]);\n\n throw new RateLimitException(\n 'Hubspot rate limit reached for configuration ' . $this->config->getId(),\n $retryAfter,\n );\n }\n\n $this->rateLimiter->incrementRequestCount($this->config);\n\n try {\n return $apiCall();\n } catch (Throwable $e) {\n if ($this->isHubspotRateLimit($e)) {\n $retryAfter = $this->parseRetryAfter($e);\n\n $this->log->warning('[Hubspot] Received 429 from API', [\n 'team_id' => $this->config->team_id,\n 'config_id' => $this->config->getId(),\n 'retry_after' => $retryAfter,\n 'reason' => $e->getMessage(),\n ]);\n\n throw new RateLimitException('Hubspot returned 429', $retryAfter, $e);\n }\n\n throw $e;\n }\n }\n\n private function isHubspotRateLimit(Throwable $e): bool\n {\n return method_exists($e, 'getCode') && (int) $e->getCode() === 429;\n }\n\n private function parseRetryAfter(Throwable $e): int\n {\n if (method_exists($e, 'getResponseHeaders')) {\n $headers = $e->getResponseHeaders() ?: [];\n $value = $headers['Retry-After'] ?? $headers['retry-after'] ?? null;\n if (is_array($value)) {\n $value = $value[0] ?? null;\n }\n if (is_numeric($value)) {\n return (int) $value;\n }\n }\n\n return 10;\n }\n\n public function getMinimumApiVersion(): string\n {\n return self::MIN_API_VERSION;\n }\n\n public function getInstance(): Factory\n {\n return new Factory([\n 'key' => $this->accessToken,\n 'oauth2' => true,\n 'base_url' => $this->baseUrl,\n ]);\n }\n\n public function getNewInstance(): Discovery\n {\n return \\HubSpot\\Factory::createWithAccessToken($this->accessToken);\n }\n\n /**\n * Secondly and daily limits for Hubspot API\n *\n * Product Tier: Free & Starter | Professional & Enterprise | API add-on (any tier)\n * Burst: 100/10 seconds | 150/10 seconds | 200/10 seconds\n * Daily: 250,000 | 500,000 | 1,000,000\n *\n * Official documentation states: The search endpoints are rate limited to five requests per second.\n * Since with 5 RPS were still hitting secondly rate limits we lowered it to 4\n */\n public function getPaginatedData(array $payload, string $type, int $offset = 0): array\n {\n $total = 0;\n $lastId = null;\n $rows = [];\n foreach ($this->getPaginatedDataGenerator($payload, $type, $offset, $total, $lastId) as $row) {\n $rows[] = $row;\n }\n\n return ['results' => $rows, 'total' => $total, 'last_record' => $lastId];\n }\n\n /**\n * @throws HubspotException\n * @throws SocialAccountTokenInvalidException\n * @throws BadRequest\n */\n public function getPaginatedDataGenerator(\n array $payload,\n string $type,\n int $offset = 0,\n int &$total = 0,\n ?string &$lastRecordId = null\n ): \\Generator {\n return $this->paginationService->getPaginatedDataGenerator(\n $this,\n $payload,\n $type,\n $offset,\n $total,\n $lastRecordId\n );\n }\n\n /**\n * @throws DealApiException\n * @throws CrmException\n */\n public function getOpportunityById(string $crmId, array $fields): array\n {\n try {\n $deal = $this->executeRequest(fn () => $this->getNewInstance()->crm()->deals()->basicApi()->getById(\n $crmId,\n implode(',', $fields),\n 'companies,contacts'\n ));\n } catch (DealApiException $e) {\n $this->log->info('[Hubspot] Failed to fetch opportunity', [\n 'crm_id' => $crmId,\n 'reason' => $e->getMessage(),\n ]);\n\n throw $e;\n }\n\n if (! $deal instanceof DealWithAssociations) {\n throw new CrmException('Deal not found');\n }\n\n return [\n 'id' => $deal->getId(),\n 'properties' => $deal->getProperties(),\n 'associations' => $deal->getAssociations(),\n ];\n }\n\n /**\n * Generic batch read method for HubSpot objects\n *\n * @param string $objectType The object type ('deals', 'companies', 'contacts')\n * @param array<string> $crmIds Array of HubSpot object IDs (max 100)\n * @param array<string> $fields Array of property names to fetch\n *\n * @return array<string, array> Array keyed by CRM ID with object data\n */\n private function batchReadObjects(string $objectType, array $crmIds, array $fields): array\n {\n if (empty($crmIds)) {\n return [];\n }\n\n $this->validateBatchSize($objectType, $crmIds);\n $this->ensureValidToken();\n\n try {\n $batchConfig = $this->createBatchConfiguration($objectType);\n $batchReadRequest = $this->prepareBatchRequest($batchConfig, $crmIds, $fields);\n $response = $batchConfig['api']->read($batchReadRequest);\n\n $this->validateApiResponse($response, $objectType);\n\n $results = $this->processApiResults($response);\n $this->logBatchResults($objectType, $crmIds, $results);\n\n return $results;\n } catch (\\Throwable $e) {\n $this->handleBatchError($e, $objectType, $crmIds);\n }\n }\n\n private function validateBatchSize(string $objectType, array $crmIds): void\n {\n if (count($crmIds) > 100) {\n throw new \\InvalidArgumentException(\"Batch size cannot exceed 100 {$objectType}\");\n }\n }\n\n private function createBatchConfiguration(string $objectType): array\n {\n $configurations = [\n 'deals' => [\n 'batchReadRequest' => new \\HubSpot\\Client\\Crm\\Deals\\Model\\BatchReadInputSimplePublicObjectId(),\n 'inputClass' => \\HubSpot\\Client\\Crm\\Deals\\Model\\SimplePublicObjectId::class,\n 'api' => $this->getNewInstance()->crm()->deals()->batchApi(),\n ],\n 'companies' => [\n 'batchReadRequest' => new \\HubSpot\\Client\\Crm\\Companies\\Model\\BatchReadInputSimplePublicObjectId(),\n 'inputClass' => \\HubSpot\\Client\\Crm\\Companies\\Model\\SimplePublicObjectId::class,\n 'api' => $this->getNewInstance()->crm()->companies()->batchApi(),\n ],\n 'contacts' => [\n 'batchReadRequest' => new \\HubSpot\\Client\\Crm\\Contacts\\Model\\BatchReadInputSimplePublicObjectId(),\n 'inputClass' => \\HubSpot\\Client\\Crm\\Contacts\\Model\\SimplePublicObjectId::class,\n 'api' => $this->getNewInstance()->crm()->contacts()->batchApi(),\n ],\n ];\n\n if (! isset($configurations[$objectType])) {\n throw new \\InvalidArgumentException(\"Unsupported object type: {$objectType}\");\n }\n\n return $configurations[$objectType];\n }\n\n private function prepareBatchRequest(array $batchConfig, array $crmIds, array $fields): object\n {\n $batchReadRequest = $batchConfig['batchReadRequest'];\n $inputClass = $batchConfig['inputClass'];\n\n $inputs = array_map(function ($crmId) use ($inputClass) {\n $input = new $inputClass();\n $input->setId($crmId);\n\n return $input;\n }, $crmIds);\n\n $batchReadRequest->setInputs($inputs);\n $batchReadRequest->setProperties($fields);\n\n return $batchReadRequest;\n }\n\n private function validateApiResponse($response, string $objectType): void\n {\n if (! $response) {\n throw new CrmException(\"HubSpot API returned null response for {$objectType} batch read\");\n }\n }\n\n private function processApiResults($response): array\n {\n $results = [];\n $responseResults = $response->getResults();\n\n if ($responseResults) {\n foreach ($responseResults as $object) {\n if ($object && $object->getId()) {\n $results[$object->getId()] = [\n 'id' => $object->getId(),\n 'properties' => $object->getProperties() ?: [],\n ];\n }\n }\n }\n\n return $results;\n }\n\n private function logBatchResults(string $objectType, array $crmIds, array $results): void\n {\n $this->log->info(\"[HubSpot] Batch fetched {$objectType}\", [\n 'requested_count' => count($crmIds),\n 'returned_count' => count($results),\n 'crm_ids' => $crmIds,\n ]);\n }\n\n private function handleBatchError(\\Throwable $e, string $objectType, array $crmIds): void\n {\n $errorMessage = $e->getMessage() ?: 'Unknown error';\n $errorTrace = $e->getTraceAsString() ?: 'No trace available';\n\n $this->log->error(\"[HubSpot] Failed to batch fetch {$objectType}\", [\n 'crm_ids' => $crmIds,\n 'error' => $errorMessage,\n 'trace' => $errorTrace,\n ]);\n\n throw new CrmException(\"Failed to batch fetch {$objectType}: \" . $errorMessage);\n }\n\n /**\n * Batch read multiple opportunities by their CRM IDs\n *\n * @param array<string> $crmIds Array of HubSpot deal IDs (max 100)\n * @param array<string> $fields Array of property names to fetch\n *\n * @return array<string, array> Array keyed by CRM ID with opportunity data\n */\n public function getOpportunitiesByIds(array $crmIds, array $fields): array\n {\n return $this->batchReadObjects('deals', $crmIds, $fields);\n }\n\n /**\n * Batch read multiple companies by their CRM IDs\n *\n * @param array<string> $crmIds Array of HubSpot company IDs (max 100)\n * @param array<string> $fields Array of property names to fetch\n *\n * @return array<string, array> Array keyed by CRM ID with company data\n */\n public function getCompaniesByIds(array $crmIds, array $fields): array\n {\n return $this->batchReadObjects('companies', $crmIds, $fields);\n }\n\n /**\n * Batch read multiple contacts by their CRM IDs\n *\n * @param array<string> $crmIds Array of HubSpot contact IDs (max 100)\n * @param array<string> $fields Array of property names to fetch\n *\n * @return array<string, array> Array keyed by CRM ID with contact data\n */\n public function getContactsByIds(array $crmIds, array $fields): array\n {\n return $this->batchReadObjects('contacts', $crmIds, $fields);\n }\n\n /**\n * @throws CompanyApiException\n * @throws CrmException\n */\n public function getAccountById(string $crmId, array $fields): array\n {\n try {\n $company = $this->getNewInstance()->crm()->companies()->basicApi()->getById(\n $crmId,\n implode(',', $fields),\n );\n } catch (CompanyApiException $e) {\n $this->log->info('[Hubspot] Failed to fetch account', [\n 'crm_id' => $crmId,\n 'reason' => $e->getMessage(),\n ]);\n\n throw $e;\n }\n\n if (! $company instanceof CompaniesWithAssociations) {\n throw new CrmException('Account not found');\n }\n\n return [\n 'id' => $company->getId(),\n 'properties' => $company->getProperties(),\n ];\n }\n\n /**\n * @throws ContactApiException\n * @throws CrmException\n */\n public function getContactById(string $crmId, array $fields): array\n {\n try {\n $contact = $this->getNewInstance()->crm()->contacts()->basicApi()->getById(\n $crmId,\n implode(',', $fields)\n );\n } catch (ContactApiException $e) {\n $this->log->info('[Hubspot] Failed to fetch contact', [\n 'crm_id' => $crmId,\n 'reason' => $e->getMessage(),\n ]);\n\n throw $e;\n }\n\n if (! $contact instanceof ContactsWithAssociations) {\n throw new CrmException('Contact not found');\n }\n\n return [\n 'id' => $contact->getId(),\n 'properties' => $contact->getProperties(),\n ];\n }\n\n /**\n * This is email search request that Hubspot offers as GET (more generous quota)\n */\n public function getContactByEmail(string $email, array $fields = []): array\n {\n try {\n $contact = $this->getNewInstance()->crm()->contacts()->basicApi()->getById(\n $email,\n implode(',', $fields),\n null,\n false,\n 'email'\n );\n\n return [\n 'id' => $contact->getId(),\n 'properties' => $contact->getProperties(),\n ];\n } catch (ContactApiException $e) {\n $this->log->info('[Hubspot] Failed to fetch contact', [\n 'email' => $email,\n 'reason' => $e->getMessage(),\n ]);\n\n return [];\n }\n }\n\n /**\n * @throws CrmException\n */\n public function fetchProperty(string $objectType, string $propertyId): Property\n {\n $result = $this->getNewInstance()->crm()->properties()->coreApi()->getByName($objectType, $propertyId);\n\n if (! $result instanceof Property) {\n $this->log->error('[Hubspot] Failed to fetch property', [\n 'object_type' => $objectType,\n 'property_id' => $propertyId,\n 'reason' => $result->getMessage(),\n ]);\n\n throw new CrmException('Failed to fetch property');\n }\n\n return $result;\n }\n\n /**\n * @return array<CrmFieldOption>\n */\n public function fetchPropertyOptions(string $objectType, string $propertyId): array\n {\n /** @var array<CrmFieldOption> */\n return $this->fetchProperty($objectType, $propertyId)->getOptions();\n }\n\n /**\n * @return array<array{id:string, label:string, deleted:bool}>\n */\n public function fetchCallDispositions(): array\n {\n /** @var Response $response */\n $response = $this->getInstance()->engagements()->getCallDispositions();\n\n /**\n * @var array<array{\n * id:string,\n * label:string,\n * deleted: bool\n * }>\n */\n return $response->toArray();\n }\n\n /**\n * @return array<CrmFieldOption>\n */\n public function fetchOpportunityPipelineStages(): array\n {\n $stages = [];\n $apiResponse = $this->getNewInstance()->crm()->pipelines()->pipelinesApi()->getAll('deals');\n\n if ($apiResponse instanceof Error) {\n $this->log->error('[Hubspot] Failed to fetch opportunity pipelines', [\n 'reason' => $apiResponse->getMessage(),\n ]);\n\n return [];\n }\n\n foreach ($apiResponse->getResults() as $pipeline) {\n $pipelineStages = array_map(\n static function (PipelineStage $stage) {\n return [\n 'id' => $stage->getId(),\n 'label' => $stage->getLabel(),\n ];\n },\n $pipeline->getStages()\n );\n\n $stages = array_merge($stages, $pipelineStages);\n }\n\n return $stages;\n }\n\n public function fetchOpportunityPipelines(): array\n {\n $pipelines = [];\n\n try {\n $apiResponse = $this->makeRequest('/crm/v3/pipelines/deals');\n } catch (\\Exception $e) {\n $this->log->info('[Hubspot] Failed to fetch opportunity pipelines', [\n 'reason' => $e->getMessage(),\n ]);\n\n return [];\n }\n\n $response = $apiResponse->toArray();\n\n foreach ($response['results'] as $pipeline) {\n $pipelines[] = [\n 'id' => $pipeline['id'],\n 'label' => $pipeline['label'],\n ];\n }\n\n return $pipelines;\n }\n\n /**\n * @return array<CrmFieldOption>\n */\n public function fetchMeetingOutcomeFieldOptions(Field $field): array\n {\n return $field->getCrmProviderId() === 'meetingOutcome'\n ? $this->fetchMeetingOutcomeTypes()\n : $this->fetchCallActivityTypes();\n }\n\n public function fetchMeetingOutcomeTypes(): array\n {\n return $this->extractMeetingTypeOptions(\n 'https://api.hubapi.com/crm/v3/properties/meeting/hs_meeting_outcome'\n );\n }\n\n public function fetchCallActivityTypes(): array\n {\n return $this->extractMeetingTypeOptions(\n 'https://api.hubapi.com/crm/v3/properties/call/hs_activity_type'\n );\n }\n\n private function extractMeetingTypeOptions(string $endpoint): array\n {\n /** @var Response $response */\n $response = $this->getInstance()\n ->getClient()\n ->request('GET', $endpoint);\n\n /**\n * @var array<array{\n * value: string,\n * label: string,\n * displayOrder: int\n * }> $optionData\n */\n $optionData = $response->toArray()['options'] ?? [];\n\n $options = [];\n foreach ($optionData as $item) {\n $options[] = [\n 'id' => $item['value'],\n 'value' => $item['value'],\n 'label' => $item['label'],\n 'display_order' => $item['displayOrder'],\n ];\n }\n\n return $options;\n }\n\n /**\n * @return array<CrmFieldOption>\n */\n public function fetchDispositionFieldOptions(): array\n {\n $options = [];\n\n $dispositions = $this->fetchCallDispositions();\n\n foreach ($dispositions as $disposition) {\n if ($disposition['deleted'] !== false) {\n continue;\n }\n\n $option['value'] = $disposition['id'];\n $option['id'] = $disposition['id'];\n $option['label'] = $disposition['label'];\n\n $options[] = $option;\n }\n\n return $options;\n }\n\n /**\n * @return array<CrmFieldOption>\n */\n public function fetchOpportunityFieldOptions(Field $field): array\n {\n if ($field->isStageField()) {\n return $this->fetchOpportunityPipelineStages();\n }\n\n if ($field->isPipelineField()) {\n return $this->fetchOpportunityPipelines();\n }\n\n return $this->fetchPropertyOptions('deals', $field->getCrmProviderId());\n }\n\n /**\n * @throws BadRequest\n * @throws HubspotException\n */\n public function makeRequest(string $endpoint, $method = 'GET', $payload = [], ?string $queryString = null)\n {\n $endpoint = self::BASE_URL . $endpoint;\n\n if ($method === 'GET') {\n return $this->getInstance()->getClient()?->request(\n method: $method,\n endpoint: $endpoint,\n query_string: $queryString\n );\n } else {\n return $this->getInstance()->getClient()->request($method, $endpoint, [\n 'json' => ($payload),\n ]);\n }\n }\n\n /**\n * @throws BadRequest\n * @throws HubspotException\n */\n public function createMeeting(array $payload): Response\n {\n $endpoint = '/crm/v3/objects/meetings';\n\n return $this->makeRequest($endpoint, 'POST', $payload);\n }\n\n /**\n * @throws BadRequest\n * @throws HubspotException\n */\n public function updateMeeting(string $meetingId, array $payload): Response\n {\n $endpoint = '/crm/v3/objects/meetings/' . $meetingId;\n\n return $this->makeRequest($endpoint, 'PATCH', $payload);\n }\n\n /**\n * @throws \\Exception\n */\n public function createNote(\n string $body,\n string $ownerId,\n int $timestamp,\n string $objectId,\n NoteObject $noteObject\n ): ?string {\n try {\n $noteInput = new SimplePublicObjectInput([\n 'properties' => [\n 'hs_note_body' => $body,\n 'hubspot_owner_id' => $ownerId,\n 'hs_timestamp' => $timestamp,\n ],\n ]);\n\n // Create note\n $note = $this->getNewInstance()->crm()->objects()->basicApi()->create('note', $noteInput);\n\n $this->getNewInstance()->crm()->objects()->associationsApi()->create(\n 'note',\n $note->getId(),\n $this->getNoteObject($noteObject),\n $objectId,\n $this->getNoteAssociationType($noteObject),\n );\n\n return $note->getId();\n } catch (\\Exception $e) {\n $this->log->error('[Hubspot] Failed to create note', [\n 'objectId' => $objectId,\n 'noteObject' => $noteObject->getObjectType(),\n 'reason' => $e->getMessage(),\n ]);\n\n \\Sentry::captureException($e);\n }\n\n return null;\n }\n\n public function updateEngagement(string $objectId, array $engagement, array $metadata): void\n {\n $this->getInstance()->engagements()->update($objectId, $engagement, $metadata);\n }\n\n public function getEngagementData(string $engagementId): array\n {\n $engagement = $this->getInstance()->engagements()->get($engagementId);\n\n return $engagement->toArray();\n }\n\n public function createEngagement(array $engagement, array $associations, array $metadata): Response\n {\n return $this->getInstance()\n ->engagements()\n ->create($engagement, $associations, $metadata);\n }\n\n public function isUnauthorizedException(\\Exception $e): bool\n {\n // Check for specific HubSpot API exception types first\n if ($e instanceof BadRequest) {\n // BadRequest can contain 401 status codes\n return $e->getCode() === 401;\n }\n\n // Check for HTTP client exceptions with status codes\n if ($e instanceof \\GuzzleHttp\\Exception\\RequestException && $e->hasResponse()) {\n $response = $e->getResponse();\n if ($response !== null) {\n return $response->getStatusCode() === 401;\n }\n }\n\n // Check for Guzzle HTTP exceptions\n if ($e instanceof \\GuzzleHttp\\Exception\\ClientException) {\n return $e->getCode() === 401;\n }\n\n // Fallback to string matching as last resort, but be more specific\n $message = strtolower($e->getMessage());\n\n return str_contains($message, '401 unauthorized') ||\n str_contains($message, 'http 401') ||\n str_contains($message, 'status code 401') ||\n (preg_match('/\\b401\\b/', $message) && str_contains($message, 'unauthorized'));\n }\n\n /**\n * Validates and refreshes the access token if needed before API requests.\n * This ensures long-running processes don't fail due to token expiration.\n *\n * @throws SocialAccountTokenInvalidException\n */\n public function ensureValidToken(): void\n {\n if ($this->oauthAccount === null) {\n return;\n }\n\n $newToken = $this->tokenManager->ensureValidToken($this->oauthAccount);\n if ($newToken !== null) {\n $this->accessToken = $newToken;\n }\n }\n\n public function getConfig()\n {\n return $this->config;\n }\n\n // returns only active (archived=false)\n public function getOwners(): array\n {\n return $this->getNewInstance()->crm()->owners()->getAll();\n }\n\n /**\n * @param bool $archived\n *\n * @return array<Owner>|[]\n */\n public function getOwnersArchived(bool $archived = true): array\n {\n $endpoint = '/crm/v3/owners';\n $queryParams = [\n 'archived' => $archived ? 'true' : 'false',\n ];\n $queryString = http_build_query($queryParams);\n\n $owners = [];\n\n try {\n $response = $this->makeRequest(endpoint: $endpoint, queryString: $queryString);\n $responseData = $response?->toArray();\n\n foreach ($responseData['results'] as $result) {\n try {\n $owners[] = Owner::create($result);\n } catch (Throwable $e) {\n $this->log->error('[HubSpot] Failed to process owner data', [\n 'result' => $result,\n 'error' => $e->getMessage(),\n ]);\n\n continue;\n }\n }\n } catch (Throwable $e) {\n $this->log->error('HubSpot] Failed to fetch owners', [\n 'archived' => $archived,\n 'error' => $e->getMessage(),\n ]);\n\n return [];\n }\n\n return $owners;\n }\n\n public function getMeeting(string $engagementId): ObjectWithAssociations\n {\n return $this->getNewInstance()->crm()->objects()->basicApi()\n ->getById('meeting', $engagementId, null, 'contact,company,deal');\n }\n\n public function deleteEngagement(string $engagementId): void\n {\n $this->getInstance()->engagements()->delete((int) $engagementId);\n }\n\n public function getAssociationsData(array $ids, string $fromObject, string $toObject): array\n {\n $associationData = [];\n $idChunks = array_chunk($ids, self::ASSOCIATIONS_BATCH_SIZE_LIMIT);\n\n foreach ($idChunks as $idChunk) {\n try {\n $batchInput = new \\HubSpot\\Client\\Crm\\Associations\\Model\\BatchInputPublicObjectId();\n $batchInput->setInputs(array_map(function ($id) {\n $publicObjectId = new \\HubSpot\\Client\\Crm\\Associations\\Model\\PublicObjectId();\n $publicObjectId->setId($id);\n\n return $publicObjectId;\n }, $idChunk));\n\n $associatedObjectsData = $this\n ->getNewInstance()\n ->crm()\n ->associations()\n ->batchApi()\n ->read($fromObject, $toObject, $batchInput);\n\n if ($associatedObjectsData instanceof \\HubSpot\\Client\\Crm\\Associations\\Model\\BatchResponsePublicAssociationMulti) {\n foreach ($associatedObjectsData->getResults() as $association) {\n $from = $association->getFrom()->getId();\n $toAssociations = $association->getTo();\n\n if (! empty($toAssociations)) {\n $associationData[$from] = array_map(function ($item) {\n return $item->getId();\n }, $toAssociations);\n }\n }\n }\n } catch (\\Exception $e) {\n $this->log->error('[Hubspot] Failed to fetch associations', [\n 'from_object' => $fromObject,\n 'to_object' => $toObject,\n 'reason' => $e->getMessage(),\n ]);\n }\n }\n\n return $associationData;\n }\n\n /**\n * @throws \\Exception\n */\n private function getNoteAssociationType(NoteObject $noteObject): string\n {\n return match($noteObject) {\n NoteObject::Opportunity => 'note_to_deal',\n NoteObject::Lead, NoteObject::Contact => 'note_to_contact', // or 'note_to_lead' if your portal supports it\n NoteObject::Account => 'note_to_company',\n NoteObject::Call, NoteObject::Event => throw new \\Exception('Not supported'),\n };\n }\n\n /**\n * @throws \\Exception\n */\n private function getNoteObject(NoteObject $noteObject): string\n {\n return match($noteObject) {\n NoteObject::Opportunity => 'deal',\n NoteObject::Lead, NoteObject::Contact => 'contact',\n NoteObject::Account => 'company',\n NoteObject::Call, NoteObject::Event => throw new \\Exception('Not supported'),\n };\n }\n\n public function addAssociations(string $objectType, string $associationType, array $payload): Response\n {\n $endpoint = \"/crm/v4/associations/$objectType/$associationType/batch/create\";\n\n return $this->makeRequest($endpoint, 'POST', $payload);\n }\n\n public function removeAssociations(string $objectType, string $associationType, array $payload): Response\n {\n $endpoint = \"/crm/v4/associations/$objectType/$associationType/batch/archive\";\n\n return $this->makeRequest($endpoint, 'POST', $payload);\n }\n}","role_description":"text entry area","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Project","depth":3,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Project","depth":3,"bounds":{"left":0.011968086,"top":0.047885075,"width":0.024268618,"height":0.024740623},"on_screen":true,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"New File or Directory…","depth":4,"bounds":{"left":0.27027926,"top":1.0,"width":0.008643617,"height":0.0},"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Expand Selected","depth":4,"bounds":{"left":0.27027926,"top":1.0,"width":0.008643617,"height":0.0},"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Collapse All","depth":4,"bounds":{"left":0.27027926,"top":1.0,"width":0.008643617,"height":0.0},"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Options","depth":4,"bounds":{"left":0.27027926,"top":1.0,"width":0.008643617,"height":0.0},"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Hide","depth":4,"bounds":{"left":0.27027926,"top":1.0,"width":0.008643617,"height":0.0},"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false}]...
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# [PASSWORD_DOTS] HS [PASSWORD_DOTS]
select * from teams where id = 2; # 2
select * from features; # 2
select * from team_features where team_id = 2; # 2
select * from crm_configurations where id = 2; # 2
select * from users where team_id = 2; #
select * from playbooks where team_id = 2; # event 38
select * from playbook_categories where playbook_id = 38; #
SELECT * FROM activities WHERE crm_configuration_id = 2 and crm_provider_id is not null order by id desc;
[URL_WITH_CREDENTIALS] CrmFieldOption array{id:string, label:string, value?:string}
*/
class Client extends BaseClient implements HubspotClientInterface
{
public const string MIN_API_VERSION = '2';
public const string BASE_URL = '[URL_WITH_CREDENTIALS] T
* @param callable(): T $apiCall
* @return T
*
* @throws RateLimitException
*/
private function executeRequest(callable $apiCall)
{
if (! $this->rateLimiter->canMakeRequest($this->config)) {
$retryAfter = $this->rateLimiter->requestAvailableIn($this->config);
$this->log->warning('[Hubspot] Rate limit exceeded, deferring request', [
'team_id' => $this->config->team_id,
'config_id' => $this->config->getId(),
'retry_after' => $retryAfter,
]);
throw new RateLimitException(
'Hubspot rate limit reached for configuration ' . $this->config->getId(),
$retryAfter,
);
}
$this->rateLimiter->incrementRequestCount($this->config);
try {
return $apiCall();
} catch (Throwable $e) {
if ($this->isHubspotRateLimit($e)) {
$retryAfter = $this->parseRetryAfter($e);
$this->log->warning('[Hubspot] Received 429 from API', [
'team_id' => $this->config->team_id,
'config_id' => $this->config->getId(),
'retry_after' => $retryAfter,
'reason' => $e->getMessage(),
]);
throw new RateLimitException('Hubspot returned 429', $retryAfter, $e);
}
throw $e;
}
}
private function isHubspotRateLimit(Throwable $e): bool
{
return method_exists($e, 'getCode') && (int) $e->getCode() === 429;
}
private function parseRetryAfter(Throwable $e): int
{
if (method_exists($e, 'getResponseHeaders')) {
$headers = $e->getResponseHeaders() ?: [];
$value = $headers['Retry-After'] ?? $headers['retry-after'] ?? null;
if (is_array($value)) {
$value = $value[0] ?? null;
}
if (is_numeric($value)) {
return (int) $value;
}
}
return 10;
}
public function getMinimumApiVersion(): string
{
return self::MIN_API_VERSION;
}
public function getInstance(): Factory
{
return new Factory([
'key' => $this->accessToken,
'oauth2' => true,
'base_url' => $this->baseUrl,
]);
}
public function getNewInstance(): Discovery
{
return \HubSpot\Factory::createWithAccessToken($this->accessToken);
}
/**
* Secondly and daily limits for Hubspot API
*
* Product Tier: Free & Starter | Professional & Enterprise | API add-on (any tier)
* Burst: 100/10 seconds | 150/10 seconds | 200/10 seconds
* Daily: 250,000 | 500,000 | 1,000,000
*
* Official documentation states: The search endpoints are rate limited to five requests per second.
* Since with 5 RPS were still hitting secondly rate limits we lowered it to 4
*/
public function getPaginatedData(array $payload, string $type, int $offset = 0): array
{
$total = 0;
$lastId = null;
$rows = [];
foreach ($this->getPaginatedDataGenerator($payload, $type, $offset, $total, $lastId) as $row) {
$rows[] = $row;
}
return ['results' => $rows, 'total' => $total, 'last_record' => $lastId];
}
/**
* @throws HubspotException
* @throws SocialAccountTokenInvalidException
* @throws BadRequest
*/
public function getPaginatedDataGenerator(
array $payload,
string $type,
int $offset = 0,
int &$total = 0,
?string &$lastRecordId = null
): \Generator {
return $this->paginationService->getPaginatedDataGenerator(
$this,
$payload,
$type,
$offset,
$total,
$lastRecordId
);
}
/**
* @throws DealApiException
* @throws CrmException
*/
public function getOpportunityById(string $crmId, array $fields): array
{
try {
$deal = $this->executeRequest(fn () => $this->getNewInstance()->crm()->deals()->basicApi()->getById(
$crmId,
implode(',', $fields),
'companies,contacts'
));
} catch (DealApiException $e) {
$this->log->info('[Hubspot] Failed to fetch opportunity', [
'crm_id' => $crmId,
'reason' => $e->getMessage(),
]);
throw $e;
}
if (! $deal instanceof DealWithAssociations) {
throw new CrmException('Deal not found');
}
return [
'id' => $deal->getId(),
'properties' => $deal->getProperties(),
'associations' => $deal->getAssociations(),
];
}
/**
* Generic batch read method for HubSpot objects
*
* @param string $objectType The object type ('deals', 'companies', 'contacts')
* @param array<string> $crmIds Array of HubSpot object IDs (max 100)
* @param array<string> $fields Array of property names to fetch
*
* @return array<string, array> Array keyed by CRM ID with object data
*/
private function batchReadObjects(string $objectType, array $crmIds, array $fields): array
{
if (empty($crmIds)) {
return [];
}
$this->validateBatchSize($objectType, $crmIds);
$this->ensureValidToken();
try {
$batchConfig = $this->createBatchConfiguration($objectType);
$batchReadRequest = $this->prepareBatchRequest($batchConfig, $crmIds, $fields);
$response = $batchConfig['api']->read($batchReadRequest);
$this->validateApiResponse($response, $objectType);
$results = $this->processApiResults($response);
$this->logBatchResults($objectType, $crmIds, $results);
return $results;
} catch (\Throwable $e) {
$this->handleBatchError($e, $objectType, $crmIds);
}
}
private function validateBatchSize(string $objectType, array $crmIds): void
{
if (count($crmIds) > 100) {
throw new \InvalidArgumentException("Batch size cannot exceed 100 {$objectType}");
}
}
private function createBatchConfiguration(string $objectType): array
{
$configurations = [
'deals' => [
'batchReadRequest' => new \HubSpot\Client\Crm\Deals\Model\BatchReadInputSimplePublicObjectId(),
'inputClass' => \HubSpot\Client\Crm\Deals\Model\SimplePublicObjectId::class,
'api' => $this->getNewInstance()->crm()->deals()->batchApi(),
],
'companies' => [
'batchReadRequest' => new \HubSpot\Client\Crm\Companies\Model\BatchReadInputSimplePublicObjectId(),
'inputClass' => \HubSpot\Client\Crm\Companies\Model\SimplePublicObjectId::class,
'api' => $this->getNewInstance()->crm()->companies()->batchApi(),
],
'contacts' => [
'batchReadRequest' => new \HubSpot\Client\Crm\Contacts\Model\BatchReadInputSimplePublicObjectId(),
'inputClass' => \HubSpot\Client\Crm\Contacts\Model\SimplePublicObjectId::class,
'api' => $this->getNewInstance()->crm()->contacts()->batchApi(),
],
];
if (! isset($configurations[$objectType])) {
throw new \InvalidArgumentException("Unsupported object type: {$objectType}");
}
return $configurations[$objectType];
}
private function prepareBatchRequest(array $batchConfig, array $crmIds, array $fields): object
{
$batchReadRequest = $batchConfig['batchReadRequest'];
$inputClass = $batchConfig['inputClass'];
$inputs = array_map(function ($crmId) use ($inputClass) {
$input = new $inputClass();
$input->setId($crmId);
return $input;
}, $crmIds);
$batchReadRequest->setInputs($inputs);
$batchReadRequest->setProperties($fields);
return $batchReadRequest;
}
private function validateApiResponse($response, string $objectType): void
{
if (! $response) {
throw new CrmException("HubSpot API returned null response for {$objectType} batch read");
}
}
private function processApiResults($response): array
{
$results = [];
$responseResults = $response->getResults();
if ($responseResults) {
foreach ($responseResults as $object) {
if ($object && $object->getId()) {
$results[$object->getId()] = [
'id' => $object->getId(),
'properties' => $object->getProperties() ?: [],
];
}
}
}
return $results;
}
private function logBatchResults(string $objectType, array $crmIds, array $results): void
{
$this->log->info("[HubSpot] Batch fetched {$objectType}", [
'requested_count' => count($crmIds),
'returned_count' => count($results),
'crm_ids' => $crmIds,
]);
}
private function handleBatchError(\Throwable $e, string $objectType, array $crmIds): void
{
$errorMessage = $e->getMessage() ?: 'Unknown error';
$errorTrace = $e->getTraceAsString() ?: 'No trace available';
$this->log->error("[HubSpot] Failed to batch fetch {$objectType}", [
'crm_ids' => $crmIds,
'error' => $errorMessage,
'trace' => $errorTrace,
]);
throw new CrmException("Failed to batch fetch {$objectType}: " . $errorMessage);
}
/**
* Batch read multiple opportunities by their CRM IDs
*
* @param array<string> $crmIds Array of HubSpot deal IDs (max 100)
* @param array<string> $fields Array of property names to fetch
*
* @return array<string, array> Array keyed by CRM ID with opportunity data
*/
public function getOpportunitiesByIds(array $crmIds, array $fields): array
{
return $this->batchReadObjects('deals', $crmIds, $fields);
}
/**
* Batch read multiple companies by their CRM IDs
*
* @param array<string> $crmIds Array of HubSpot company IDs (max 100)
* @param array<string> $fields Array of property names to fetch
*
* @return array<string, array> Array keyed by CRM ID with company data
*/
public function getCompaniesByIds(array $crmIds, array $fields): array
{
return $this->batchReadObjects('companies', $crmIds, $fields);
}
/**
* Batch read multiple contacts by their CRM IDs
*
* @param array<string> $crmIds Array of HubSpot contact IDs (max 100)
* @param array<string> $fields Array of property names to fetch
*
* @return array<string, array> Array keyed by CRM ID with contact data
*/
public function getContactsByIds(array $crmIds, array $fields): array
{
return $this->batchReadObjects('contacts', $crmIds, $fields);
}
/**
* @throws CompanyApiException
* @throws CrmException
*/
public function getAccountById(string $crmId, array $fields): array
{
try {
$company = $this->getNewInstance()->crm()->companies()->basicApi()->getById(
$crmId,
implode(',', $fields),
);
} catch (CompanyApiException $e) {
$this->log->info('[Hubspot] Failed to fetch account', [
'crm_id' => $crmId,
'reason' => $e->getMessage(),
]);
throw $e;
}
if (! $company instanceof CompaniesWithAssociations) {
throw new CrmException('Account not found');
}
return [
'id' => $company->getId(),
'properties' => $company->getProperties(),
];
}
/**
* @throws ContactApiException
* @throws CrmException
*/
public function getContactById(string $crmId, array $fields): array
{
try {
$contact = $this->getNewInstance()->crm()->contacts()->basicApi()->getById(
$crmId,
implode(',', $fields)
);
} catch (ContactApiException $e) {
$this->log->info('[Hubspot] Failed to fetch contact', [
'crm_id' => $crmId,
'reason' => $e->getMessage(),
]);
throw $e;
}
if (! $contact instanceof ContactsWithAssociations) {
throw new CrmException('Contact not found');
}
return [
'id' => $contact->getId(),
'properties' => $contact->getProperties(),
];
}
/**
* This is email search request that Hubspot offers as GET (more generous quota)
*/
public function getContactByEmail(string $email, array $fields = []): array
{
try {
$contact = $this->getNewInstance()->crm()->contacts()->basicApi()->getById(
$email,
implode(',', $fields),
null,
false,
'email'
);
return [
'id' => $contact->getId(),
'properties' => $contact->getProperties(),
];
} catch (ContactApiException $e) {
$this->log->info('[Hubspot] Failed to fetch contact', [
'email' => $email,
'reason' => $e->getMessage(),
]);
return [];
}
}
/**
* @throws CrmException
*/
public function fetchProperty(string $objectType, string $propertyId): Property
{
$result = $this->getNewInstance()->crm()->properties()->coreApi()->getByName($objectType, $propertyId);
if (! $result instanceof Property) {
$this->log->error('[Hubspot] Failed to fetch property', [
'object_type' => $objectType,
'property_id' => $propertyId,
'reason' => $result->getMessage(),
]);
throw new CrmException('Failed to fetch property');
}
return $result;
}
/**
* @return array<CrmFieldOption>
*/
public function fetchPropertyOptions(string $objectType, string $propertyId): array
{
/** @var array<CrmFieldOption> */
return $this->fetchProperty($objectType, $propertyId)->getOptions();
}
/**
* @return array<array{id:string, label:string, deleted:bool}>
*/
public function fetchCallDispositions(): array
{
/** @var Response $response */
$response = $this->getInstance()->engagements()->getCallDispositions();
/**
* @var array<array{
* id:string,
* label:string,
* deleted: bool
* }>
*/
return $response->toArray();
}
/**
* @return array<CrmFieldOption>
*/
public function fetchOpportunityPipelineStages(): array
{
$stages = [];
$apiResponse = $this->getNewInstance()->crm()->pipelines()->pipelinesApi()->getAll('deals');
if ($apiResponse instanceof Error) {
$this->log->error('[Hubspot] Failed to fetch opportunity pipelines', [
'reason' => $apiResponse->getMessage(),
]);
return [];
}
foreach ($apiResponse->getResults() as $pipeline) {
$pipelineStages = array_map(
static function (PipelineStage $stage) {
return [
'id' => $stage->getId(),
'label' => $stage->getLabel(),
];
},
$pipeline->getStages()
);
$stages = array_merge($stages, $pipelineStages);
}
return $stages;
}
public function fetchOpportunityPipelines(): array
{
$pipelines = [];
try {
$apiResponse = $this->makeRequest('/crm/v3/pipelines/deals');
} catch (\Exception $e) {
$this->log->info('[Hubspot] Failed to fetch opportunity pipelines', [
'reason' => $e->getMessage(),
]);
return [];
}
$response = $apiResponse->toArray();
foreach ($response['results'] as $pipeline) {
$pipelines[] = [
'id' => $pipeline['id'],
'label' => $pipeline['label'],
];
}
return $pipelines;
}
/**
* @return array<CrmFieldOption>
*/
public function fetchMeetingOutcomeFieldOptions(Field $field): array
{
return $field->getCrmProviderId() === 'meetingOutcome'
? $this->fetchMeetingOutcomeTypes()
: $this->fetchCallActivityTypes();
}
public function fetchMeetingOutcomeTypes(): array
{
return $this->extractMeetingTypeOptions(
'[URL_WITH_CREDENTIALS] Response $response */
$response = $this->getInstance()
->getClient()
->request('GET', $endpoint);
/**
* @var array<array{
* value: string,
* label: string,
* displayOrder: int
* }> $optionData
*/
$optionData = $response->toArray()['options'] ?? [];
$options = [];
foreach ($optionData as $item) {
$options[] = [
'id' => $item['value'],
'value' => $item['value'],
'label' => $item['label'],
'display_order' => $item['displayOrder'],
];
}
return $options;
}
/**
* @return array<CrmFieldOption>
*/
public function fetchDispositionFieldOptions(): array
{
$options = [];
$dispositions = $this->fetchCallDispositions();
foreach ($dispositions as $disposition) {
if ($disposition['deleted'] !== false) {
continue;
}
$option['value'] = $disposition['id'];
$option['id'] = $disposition['id'];
$option['label'] = $disposition['label'];
$options[] = $option;
}
return $options;
}
/**
* @return array<CrmFieldOption>
*/
public function fetchOpportunityFieldOptions(Field $field): array
{
if ($field->isStageField()) {
return $this->fetchOpportunityPipelineStages();
}
if ($field->isPipelineField()) {
return $this->fetchOpportunityPipelines();
}
return $this->fetchPropertyOptions('deals', $field->getCrmProviderId());
}
/**
* @throws BadRequest
* @throws HubspotException
*/
public function makeRequest(string $endpoint, $method = 'GET', $payload = [], ?string $queryString = null)
{
$endpoint = self::BASE_URL . $endpoint;
if ($method === 'GET') {
return $this->getInstance()->getClient()?->request(
method: $method,
endpoint: $endpoint,
query_string: $queryString
);
} else {
return $this->getInstance()->getClient()->request($method, $endpoint, [
'json' => ($payload),
]);
}
}
/**
* @throws BadRequest
* @throws HubspotException
*/
public function createMeeting(array $payload): Response
{
$endpoint = '/crm/v3/objects/meetings';
return $this->makeRequest($endpoint, 'POST', $payload);
}
/**
* @throws BadRequest
* @throws HubspotException
*/
public function updateMeeting(string $meetingId, array $payload): Response
{
$endpoint = '/crm/v3/objects/meetings/' . $meetingId;
return $this->makeRequest($endpoint, 'PATCH', $payload);
}
/**
* @throws \Exception
*/
public function createNote(
string $body,
string $ownerId,
int $timestamp,
string $objectId,
NoteObject $noteObject
): ?string {
try {
$noteInput = new SimplePublicObjectInput([
'properties' => [
'hs_note_body' => $body,
'hubspot_owner_id' => $ownerId,
'hs_timestamp' => $timestamp,
],
]);
// Create note
$note = $this->getNewInstance()->crm()->objects()->basicApi()->create('note', $noteInput);
$this->getNewInstance()->crm()->objects()->associationsApi()->create(
'note',
$note->getId(),
$this->getNoteObject($noteObject),
$objectId,
$this->getNoteAssociationType($noteObject),
);
return $note->getId();
} catch (\Exception $e) {
$this->log->error('[Hubspot] Failed to create note', [
'objectId' => $objectId,
'noteObject' => $noteObject->getObjectType(),
'reason' => $e->getMessage(),
]);
\Sentry::captureException($e);
}
return null;
}
public function updateEngagement(string $objectId, array $engagement, array $metadata): void
{
$this->getInstance()->engagements()->update($objectId, $engagement, $metadata);
}
public function getEngagementData(string $engagementId): array
{
$engagement = $this->getInstance()->engagements()->get($engagementId);
return $engagement->toArray();
}
public function createEngagement(array $engagement, array $associations, array $metadata): Response
{
return $this->getInstance()
->engagements()
->create($engagement, $associations, $metadata);
}
public function isUnauthorizedException(\Exception $e): bool
{
// Check for specific HubSpot API exception types first
if ($e instanceof BadRequest) {
// BadRequest can contain 401 status codes
return $e->getCode() === 401;
}
// Check for HTTP client exceptions with status codes
if ($e instanceof \GuzzleHttp\Exception\RequestException && $e->hasResponse()) {
$response = $e->getResponse();
if ($response !== null) {
return $response->getStatusCode() === 401;
}
}
// Check for Guzzle HTTP exceptions
if ($e instanceof \GuzzleHttp\Exception\ClientException) {
return $e->getCode() === 401;
}
// Fallback to string matching as last resort, but be more specific
$message = strtolower($e->getMessage());
return str_contains($message, '401 unauthorized') ||
str_contains($message, 'http 401') ||
str_contains($message, 'status code 401') ||
(preg_match('/\b401\b/', $message) && str_contains($message, 'unauthorized'));
}
/**
* Validates and refreshes the access token if needed before API requests.
* This ensures long-running processes don't fail due to token expiration.
*
* @throws SocialAccountTokenInvalidException
*/
public function ensureValidToken(): void
{
if ($this->oauthAccount === null) {
return;
}
$newToken = $this->tokenManager->ensureValidToken($this->oauthAccount);
if ($newToken !== null) {
$this->accessToken = $newToken;
}
}
public function getConfig()
{
return $this->config;
}
// returns only active (archived=false)
public function getOwners(): array
{
return $this->getNewInstance()->crm()->owners()->getAll();
}
/**
* @param bool $archived
*
* @return array<Owner>|[]
*/
public function getOwnersArchived(bool $archived = true): array
{
$endpoint = '/crm/v3/owners';
$queryParams = [
'archived' => $archived ? 'true' : 'false',
];
$queryString = http_build_query($queryParams);
$owners = [];
try {
$response = $this->makeRequest(endpoint: $endpoint, queryString: $queryString);
$responseData = $response?->toArray();
foreach ($responseData['results'] as $result) {
try {
$owners[] = Owner::create($result);
} catch (Throwable $e) {
$this->log->error('[HubSpot] Failed to process owner data', [
'result' => $result,
'error' => $e->getMessage(),
]);
continue;
}
}
} catch (Throwable $e) {
$this->log->error('HubSpot] Failed to fetch owners', [
'archived' => $archived,
'error' => $e->getMessage(),
]);
return [];
}
return $owners;
}
public function getMeeting(string $engagementId): ObjectWithAssociations
{
return $this->getNewInstance()->crm()->objects()->basicApi()
->getById('meeting', $engagementId, null, 'contact,company,deal');
}
public function deleteEngagement(string $engagementId): void
{
$this->getInstance()->engagements()->delete((int) $engagementId);
}
public function getAssociationsData(array $ids, string $fromObject, string $toObject): array
{
$associationData = [];
$idChunks = array_chunk($ids, self::ASSOCIATIONS_BATCH_SIZE_LIMIT);
foreach ($idChunks as $idChunk) {
try {
$batchInput = new \HubSpot\Client\Crm\Associations\Model\BatchInputPublicObjectId();
$batchInput->setInputs(array_map(function ($id) {
$publicObjectId = new \HubSpot\Client\Crm\Associations\Model\PublicObjectId();
$publicObjectId->setId($id);
return $publicObjectId;
}, $idChunk));
$associatedObjectsData = $this
->getNewInstance()
->crm()
->associations()
->batchApi()
->read($fromObject, $toObject, $batchInput);
if ($associatedObjectsData instanceof \HubSpot\Client\Crm\Associations\Model\BatchResponsePublicAssociationMulti) {
foreach ($associatedObjectsData->getResults() as $association) {
$from = $association->getFrom()->getId();
$toAssociations = $association->getTo();
if (! empty($toAssociations)) {
$associationData[$from] = array_map(function ($item) {
return $item->getId();
}, $toAssociations);
}
}
}
} catch (\Exception $e) {
$this->log->error('[Hubspot] Failed to fetch associations', [
'from_object' => $fromObject,
'to_object' => $toObject,
'reason' => $e->getMessage(),
]);
}
}
return $associationData;
}
/**
* @throws \Exception
*/
private function getNoteAssociationType(NoteObject $noteObject): string
{
return match($noteObject) {
NoteObject::Opportunity => 'note_to_deal',
NoteObject::Lead, NoteObject::Contact => 'note_to_contact', // or 'note_to_lead' if your portal supports it
NoteObject::Account => 'note_to_company',
NoteObject::Call, NoteObject::Event => throw new \Exception('Not supported'),
};
}
/**
* @throws \Exception
*/
private function getNoteObject(NoteObject $noteObject): string
{
return match($noteObject) {
NoteObject::Opportunity => 'deal',
NoteObject::Lead, NoteObject::Contact => 'contact',
NoteObject::Account => 'company',
NoteObject::Call, NoteObject::Event => throw new \Exception('Not supported'),
};
}
public function addAssociations(string $objectType, string $associationType, array $payload): Response
{
$endpoint = "/crm/v4/associations/$objectType/$associationType/batch/create";
return $this->makeRequest($endpoint, 'POST', $payload);
}
public function removeAssociations(string $objectType, string $associationType, array $payload): Response
{
$endpoint = "/crm/v4/associations/$objectType/$associationType/batch/archive";
return $this->makeRequest($endpoint, 'POST', $payload);
}
}
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2593
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2598
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/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778153412843_m2.jpg...
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PhpStorm
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faVsco.js – Hubspot/Client.php
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True
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monitor_2
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Project: faVsco.js, menu
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[{"role":"AXButton","text" [{"role":"AXButton","text":"Project: faVsco.js, menu","depth":5,"bounds":{"left":0.025930852,"top":0.019952115,"width":0.03856383,"height":0.025538707},"on_screen":true,"help_text":"~/jiminny/app","role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false}]...
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8043719072324535154
|
-8628527368849355612
|
click
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hybrid
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NULL
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Project: faVsco.js, menu
PhostormProledeyINavigare Project: faVsco.js, menu
PhostormProledeyINavigarecodeLaravelFV faVsco.js°9 master ~Support Daily - in 30 mAskJiminnyReportActivityServiceTest -100% 12Thu 7 May 14:30:12= custom.log= laravel.logA SF jiminny@localhost]A HS_local (iminny@localhost] x& console [PROD]& console (EU]cascade© BatchSyncCollector.l© RateLimitException.php© SyncToUserPilot.phpA console [STAGING]Hubspot Rate Limitind© balchsynckealsservo clientonp© ClosedDealStagesSe © Middleware/RateLimited.phgo DealFieldsService.phTx: Auto vSELEC * FROM crm Field data WHERE crm lavout entity 1d = 9715SELEC * FROM crm Field data WHERE crm lavout entity id IN (6494.6495.6496.6497.6498.649995dojiminny vsth1s->makeremoBucketda1ly'. 250000, 86466.06 41 Y6 ^DecorateAcuiviy.ong© FieldDefinitions.phpC) ProviderRateLimiter.phpлн-C) FieldT vpeconvertenclass Cuient extends BasecLient imolements Hubspotcuientinterfacee HubspotclientintertaC) HubspotTokenManatC) PavloadBuilder.php* dreturn arrau<ermFieldootion>C) RemotecrmObiectMP ResponseNormalize.public function fetchDispositionField0ptions: arrayt...}C) Service, ono@ SvncFieldAction.ohnSELECTCONCAT(u.id, CASE WHEN V.id = t.owner_id THEN ' (owner)' ELSE "' END) AS user_idu.emailsa.*,t.owner_id FROM social_accounts saJOIN users uon u.id = sa.sociable_idJOIN teams t 1..n<->1: on t.id = u.team_idWHERE U.team id = 2 and sa.provider = 'hubspot':What haooens it No rate limits are ser? ((Detault benavior)Looking at current code:php// ProviderRateLimiter:: canMakeRequestforeach (Sprovider->getRateLimitso as SrateLimit) <if ($this=>rateLimiter->tooManyAttempts(...)) return false;return true:• If no rate Limits. returns TRUE (allows request)C) SvncRelatedActivitvi* @return array<CrmFieldOption>C) WebhookSvncBatchll683select * from social accounts where id = 1499:v D IntegrationApp/ ProviderRateLimiter::incrementRequestCountforeach (Sorovider-saetRateLimits") as Sratelimit)MAccescorspublic function fetchOpportunityFieldOptions(Field $field): array{...}> Api697 F{**select * from opportunities where team id = 2and crm provider id IN ('51317301383'):// - If no rate limits, does NOTHING (no counters incremented)> D Config> DDTO* @throws BadRequest* @throws HubspotExceptionselect * from contacts where id = 85:> D Filters> m Jobssublic function makeRequest(string Sendpoint, Snethod = "GET", Spayload = [1, Pstring Sauenystn> D ProspectSearchStrat 702. m Servicetraitssendpoint = selt::BAst UrL . sendpolnt© DataClient.php© DecorateActivity.phc© LocalSearch.phg706if (Smethod === 'GET') {return sthis->qetinstanceo->qetcllento?->requestselect * from opportunities where team id = 2 order by id desc:select * from opportunities where team id = 2 and crm provider id = '51317301383': # 5112select * from opportunities where team_id = 2 and crm_provider_id = '55976759904'; # 5112select * from opportunity contacts where opportunity 1d = 57select * from crm_field_data where object_id = 1365;SELECT * FROM com fields WHERE id IN (1405. 1407. 1972. 2128)•// ProviderRateLimniter:.requestAvailableTnreturn Sprovider->getRateLimits()->isNotEmpty()? Sprovider->getRateLimits()->map(...)->max()It no rate Limits,returns bLocalSearchInterface© RemoteSearch.phpITHI1© Service.phpquery strina: SauervStrinav C Listenersselect * from features:select * from team_features where team_id IN (1):select * from team features where feature id TN (636)•current derault benavior.• V Request is allowed (no throttling)• V No Redis counters incremented•• retryAfter = 0 secondsThis means: If vou don't manuallv seed rate limits rows for a HubSoot Contiauration, reauests ao through unthrottled.This is probably NOT what you want for production. You should:1. Create a seeder to add default rate limits for all existing HubSpot configurationsc) ConvertLeadActivitie}else {2. Add to Contiquration creation flow to auto-create defaultsc) PurceLookuocache.li, M Metadatareturn $this->getInstance()->getClient()->request($method, $endpoint, ["ison' => (Spavload)SHOW CREATE TABLE opportunity_contacts;SFLECT * CPOM onnontunitv contacte WHFPE eom nnoviden id = 11117511•3. Or implement hardcoded fallbacks in code (Option 3 above)> D Miaration• → Pioedrive715IEI AIIIv Salesforce> D FieldsM OnnortunitvMatche# $slug = 'HUBSPOT_WEBHOOK_SYNC':# $team = Jiminny \Models\Team::find(2):# $feature = Feature::query(->where('slug', $slug)->firstO:Mv PecommendationImmediate implementation (for testing)1. Use Option 3 (hybrid) - hardcoded defaults with DB override capabilit.# TeamFeature::query()->create(['feature id' => $feature-›getId,'team id' => $team->getIdO]):2. Modifv executeRequest to accent <requestTvne narameterM OnnortunitvSvneStralM ProsnectSearchStrat* Athrows RadReaues+* Athrows HubsnotExcention# hubspot webhook metrics> M ServiceTraitsC) Client nhrpublic function createMeeting(array $payload): Response{...}© DecorateActivity.phpT. [EMAIL] 1eapp/Jobs/Middleware/ D HandleRateLimit.php +42ann/lohelCrm/Doloto/ DolotoCrmEntitvTrait nhn t18© FieldDefinitions.phpselect * from opportunities where team id = 2 and crm_provider_ id IN ('374720564' '14527423589' '49908861993|SELECT * FROM opportunitv_contacts WHERE opportunitv_id = '414'.SELECT * FROM opportunity contacts WHERE crm_provider id = '131501'•select * from contacts where id in (414. 464):/# RateLimitExcention.ohn© PayloadBuilder.php(e) Drofile nhn© QueryBuilder.php* dchrows badkequest* athrows HubspotExceptionpublic function updateMletan ccept Fie see tngiet Flrray Spayioads Responsel....Ask anvthing (&+bselect * from activities where crm confiquration id = 2:<> CodeC° Adantive+0 ..arQube for INE suadections: Netect more cecurity iccuec in vour DHP filec II Try SonarQube Cloud for free /I Dowad SonarQube Server // Learn more // Don't ask again (today 10:25)Reiect alllAccent alliWN Windsurf Teams715-10UTE.8io 4 spaces...
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iTerm2
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True
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PhostormProiectVIewINavigareCodeLaravelKeractorFV PhostormProiectVIewINavigareCodeLaravelKeractorFV faVsco.js°9 master ~Support Daily - in 30 mAskJiminnyReportActivityServiceTest -100% 12Thu 7 May 14:30:15= custom.log= laravel.logA SF jiminny@localhost]A HS_local (iminny@localhost] x& console [PROD]« console [EU]cascade© BatchSyncCollector.iCo kematchactviyoncrmoojectbetach.ong(& RateLimitException.pnp© SyncToUserPilot.phpA console [STAGING]Hubspot Rate Limitind© balchsynckealsservTx: Auto vsth1s->makeremoBucketda1ly'. 250000, 86466.dojiminnyo clientonp© ClosedDealStagesSe06 41 Y6 ^(©)Middleware/RateLimited.pngSELEC * FROM crm Field data WHERE crm lavout entity 1d = 9715o DealFieldsService.phSELEC * FROM crm Field data WHERE crm lavout entity 1d IN 6494.6495.6496.6497.6498.649995DecorateAcuiviy.ong© FieldDefinitions.phpC) ProviderRateLimiter.phpлн-SELECTC) FieldT vpeconvertenclass Cuient extends BasecLient imolements HubspotcuientinterfaceCONCAT(u.id, CASE WHEN V.id = t.owner_id THEN ' (owner)' ELSE "' END) AS user_id@ HubspotClientInterfau.emailC) HubspotTokenManatsa.*,What haooens it No rate limits are ser? ((Detault benavior)Looking at current code:phpC) PavloadBuilder.php* dreturn arrau<ermFieldootion>t.owner_id FROM social_accounts saC) RemotecrmObiectMJOIN users uP ResponseNormalize.public function fetchDispositionField0ptions: arrayt...}on u.id = sa.sociable_idC) Service, ono// ProviderRateLimiter:: canMakeRequestforeach (Sprovider->getRateLimitso as SrateLimit) <if ($this=>rateLimiter->tooManyAttempts(...)) return false;JOIN teams t 1..n<->1: on t.id = u.team_id@ SvncFieldAction.ohnWHERE U.team id = 2 and sa.provider = 'hubspot':return true:/ * If no rate limits. returns TRUE (allows request)C) SvncRelatedActivitvi* areturn arraucermsiel dontion>@ WebhookSvncBatchlselect * from social accounts where id = 1499:v D IntegrationApp/ ProviderRateLimiter::incrementRequestCountforeach (Sorovider-saetRateLimits") as Sratelimit)MAccescorspublic function fetchOpportunityFieldOptions(Field $field): array{...}select * from opportunities where team id = 2> Api697and crm provider id IN ('51317301383'):> D ConfigCascade %-alCommand *1// - If no rate limits, does NOTHING (no counters incremented)* ath> DDTO* Athnowe HubenotSycontionselect * from contacts where id = 85:> D Filters>D Jobsnubiic function makeRequest (strina Sendhoint, Snethodl = GETU, Snayload E M, 2string Sauenyst> D ProspectSearchStrat 702• D ServiceTraitssendpoint = selt::BASE URL . sendpolnt:© DataClient.php© DecorateActivity.phc705if (Smethod === 'GET'){© LocalSearch.phg706select * from opportunities where team id = 2 order by id desc:select * from opportunities where team id = 2 and crm provider id = '51317301383': # 5112select * from opportunities where team_id = 2 and crm_provider_id = '55976759904'; # 5112select * from opportunity contacts where opportunity 1d = 57select * from crm_field_data where object_id = 1365;SELECT * FROM com fields WHERE id IN (1405. 1407. 1972. 2128)•// ProviderRateLimiter:.requestAvailableTnreturn Sprovider->getRateLimits()->isNotEmpty()? Sprovider->getRateLimits()->map(...)->max()It no rate Limits,returns breturn sthis->qetinstanceo->qetcllento?->requestLocalSearchInterface© RemoteSearch.phpc) Service.phpquery strina: SauervStrinav C Listenersselect * from features:select * from team_features where team_id IN (1):select * from team features where feature id TN (636)•current derault benavior.• V Request is allowed (no throttling)• V No Redis counters incremented•• retryAfter = 0 secondsThis means: If vou don't manuallv seed rate limits rows for a HubSpot Contiauration, reauests ao through unthrottledThis is probably NOT what you want for production. You should:1. Create a seeder to add default rate limits for all existing HubSpot configurationsc) ConvertLeadActivitie} else {2. Add to Confiquration creation flow to auto-create defaultsc) PurceLookuocache.lireturn Sthis->getInstance()->getClient()->request(Smethod. Sendpoint. f>M MetadataSHOW CREATE TABLE opportunity_contacts;SFLECT * CPOM onnontunitv contacte WHFPE eom nnoviden id = 11117511•3. Or implement hardcoded fallbacks in code (Option 3 above)"ison' => (soavload).>M Miaration• → Pioedrivev Salesforce> D Fields• Extract Surround (E :M OnnortunitvMatche# $slug = 'HUBSPOT_WEBHOOK_SYNC':# $team = Jiminny \Models\Team::find(2):# $feature = Feature::query(->where('slug', $slug)->firstO:Mv PecommendationImmediate implementation (for testing)1. Use Option 3 (hybrid) - hardcoded defaults with DB override capabilit.# TeamFeature::query()->create(['feature id' => $feature-›getId,'team id' => $team->getIdO]):2. Modifv executeRequest to accent <requestTvne narameterM OnnortunitvSvneStral* Athrows RadReaues+> M ProsnectSearchStrat* Athrows HubsnotExcention# hubspot webhook metrics> M ServiceTraitsC) Client nhrpublic function createMeeting(array $payload): Response{...}@ DecorateActivity.php. [EMAIL] 1eapp/Jobs/Middleware/ G HandleRateLimit.php +42ann/lohelCrm/Doloto/ DolotoCrmEntitvTrait nhn t18© FieldDefinitions.php* dchrows badkequestselect * from opportunities where team id = 2 and crm_ provider id IN ('374720564' '14527423589' '49908861993|SELECT * FROM opportunitv_contacts WHERE opportunitv_id = '414'.SELECT * FROM opportunity contacts WHERE crm_provider id = '131501'•select * from contacts where id in (414. 464):s/M RateLimitExcention.ohn© PayloadBuilder.php* athrows HubspotExceptionAsk anvthing (&+b(e) Drofile nhnselect * from activities where crm confiquration id = 2:© QueryBuilder.phppublic function updateMletan ccept Fie see tngiet Flrray Spayioads Responsel....<> CodeC° Adantive+0 ..arAube for IDE suanestionsa Detect more seawritvlssues lin vour D.Dffiles lltn SonarAube Claud for free //lDownload SonarOmbe Server /llllear more /llDonit ask adain /itodav 105251Reiect alllAccent allW Windsurf Toamc716•6 (621 charc 10 line hreakc) UTF.8io 4 spaces...
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visual_change
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ocr
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PhostormProiectVIewINavigareCodeLaravelKeractorFV PhostormProiectVIewINavigareCodeLaravelKeractorFV faVsco.js°9 master ~Support Daily - in 30 mAskJiminnyReportActivityServiceTest -100% 12Thu 7 May 14:30:15= custom.log= laravel.logA SF jiminny@localhost]A HS_local (iminny@localhost] x& console [PROD]« console [EU]cascade© BatchSyncCollector.iCo kematchactviyoncrmoojectbetach.ong(& RateLimitException.pnp© SyncToUserPilot.phpA console [STAGING]Hubspot Rate Limitind© balchsynckealsservTx: Auto vsth1s->makeremoBucketda1ly'. 250000, 86466.dojiminnyo clientonp© ClosedDealStagesSe06 41 Y6 ^(©)Middleware/RateLimited.pngSELEC * FROM crm Field data WHERE crm lavout entity 1d = 9715o DealFieldsService.phSELEC * FROM crm Field data WHERE crm lavout entity 1d IN 6494.6495.6496.6497.6498.649995DecorateAcuiviy.ong© FieldDefinitions.phpC) ProviderRateLimiter.phpлн-SELECTC) FieldT vpeconvertenclass Cuient extends BasecLient imolements HubspotcuientinterfaceCONCAT(u.id, CASE WHEN V.id = t.owner_id THEN ' (owner)' ELSE "' END) AS user_id@ HubspotClientInterfau.emailC) HubspotTokenManatsa.*,What haooens it No rate limits are ser? ((Detault benavior)Looking at current code:phpC) PavloadBuilder.php* dreturn arrau<ermFieldootion>t.owner_id FROM social_accounts saC) RemotecrmObiectMJOIN users uP ResponseNormalize.public function fetchDispositionField0ptions: arrayt...}on u.id = sa.sociable_idC) Service, ono// ProviderRateLimiter:: canMakeRequestforeach (Sprovider->getRateLimitso as SrateLimit) <if ($this=>rateLimiter->tooManyAttempts(...)) return false;JOIN teams t 1..n<->1: on t.id = u.team_id@ SvncFieldAction.ohnWHERE U.team id = 2 and sa.provider = 'hubspot':return true:/ * If no rate limits. returns TRUE (allows request)C) SvncRelatedActivitvi* areturn arraucermsiel dontion>@ WebhookSvncBatchlselect * from social accounts where id = 1499:v D IntegrationApp/ ProviderRateLimiter::incrementRequestCountforeach (Sorovider-saetRateLimits") as Sratelimit)MAccescorspublic function fetchOpportunityFieldOptions(Field $field): array{...}select * from opportunities where team id = 2> Api697and crm provider id IN ('51317301383'):> D ConfigCascade %-alCommand *1// - If no rate limits, does NOTHING (no counters incremented)* ath> DDTO* Athnowe HubenotSycontionselect * from contacts where id = 85:> D Filters>D Jobsnubiic function makeRequest (strina Sendhoint, Snethodl = GETU, Snayload E M, 2string Sauenyst> D ProspectSearchStrat 702• D ServiceTraitssendpoint = selt::BASE URL . sendpolnt:© DataClient.php© DecorateActivity.phc705if (Smethod === 'GET'){© LocalSearch.phg706select * from opportunities where team id = 2 order by id desc:select * from opportunities where team id = 2 and crm provider id = '51317301383': # 5112select * from opportunities where team_id = 2 and crm_provider_id = '55976759904'; # 5112select * from opportunity contacts where opportunity 1d = 57select * from crm_field_data where object_id = 1365;SELECT * FROM com fields WHERE id IN (1405. 1407. 1972. 2128)•// ProviderRateLimiter:.requestAvailableTnreturn Sprovider->getRateLimits()->isNotEmpty()? Sprovider->getRateLimits()->map(...)->max()It no rate Limits,returns breturn sthis->qetinstanceo->qetcllento?->requestLocalSearchInterface© RemoteSearch.phpc) Service.phpquery strina: SauervStrinav C Listenersselect * from features:select * from team_features where team_id IN (1):select * from team features where feature id TN (636)•current derault benavior.• V Request is allowed (no throttling)• V No Redis counters incremented•• retryAfter = 0 secondsThis means: If vou don't manuallv seed rate limits rows for a HubSpot Contiauration, reauests ao through unthrottledThis is probably NOT what you want for production. You should:1. Create a seeder to add default rate limits for all existing HubSpot configurationsc) ConvertLeadActivitie} else {2. Add to Confiquration creation flow to auto-create defaultsc) PurceLookuocache.lireturn Sthis->getInstance()->getClient()->request(Smethod. Sendpoint. f>M MetadataSHOW CREATE TABLE opportunity_contacts;SFLECT * CPOM onnontunitv contacte WHFPE eom nnoviden id = 11117511•3. Or implement hardcoded fallbacks in code (Option 3 above)"ison' => (soavload).>M Miaration• → Pioedrivev Salesforce> D Fields• Extract Surround (E :M OnnortunitvMatche# $slug = 'HUBSPOT_WEBHOOK_SYNC':# $team = Jiminny \Models\Team::find(2):# $feature = Feature::query(->where('slug', $slug)->firstO:Mv PecommendationImmediate implementation (for testing)1. Use Option 3 (hybrid) - hardcoded defaults with DB override capabilit.# TeamFeature::query()->create(['feature id' => $feature-›getId,'team id' => $team->getIdO]):2. Modifv executeRequest to accent <requestTvne narameterM OnnortunitvSvneStral* Athrows RadReaues+> M ProsnectSearchStrat* Athrows HubsnotExcention# hubspot webhook metrics> M ServiceTraitsC) Client nhrpublic function createMeeting(array $payload): Response{...}@ DecorateActivity.php. [EMAIL] 1eapp/Jobs/Middleware/ G HandleRateLimit.php +42ann/lohelCrm/Doloto/ DolotoCrmEntitvTrait nhn t18© FieldDefinitions.php* dchrows badkequestselect * from opportunities where team id = 2 and crm_ provider id IN ('374720564' '14527423589' '49908861993|SELECT * FROM opportunitv_contacts WHERE opportunitv_id = '414'.SELECT * FROM opportunity contacts WHERE crm_provider id = '131501'•select * from contacts where id in (414. 464):s/M RateLimitExcention.ohn© PayloadBuilder.php* athrows HubspotExceptionAsk anvthing (&+b(e) Drofile nhnselect * from activities where crm confiquration id = 2:© QueryBuilder.phppublic function updateMletan ccept Fie see tngiet Flrray Spayioads Responsel....<> CodeC° Adantive+0 ..arAube for IDE suanestionsa Detect more seawritvlssues lin vour D.Dffiles lltn SonarAube Claud for free //lDownload SonarOmbe Server /llllear more /llDonit ask adain /itodav 105251Reiect alllAccent allW Windsurf Toamc716•6 (621 charc 10 line hreakc) UTF.8io 4 spaces...
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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
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10:42
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Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
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hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
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You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
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Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
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┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │...
|
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It's a separate bucket from the 190/10s, and critically, search responses do not include the","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-*","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"headers","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— you can't read remaining capacity off the response, you have to track it yourself.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Non-search endpoints return","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Max","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", and","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Interval-Milliseconds","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". The daily headers are not returned for OAuth-authenticated calls","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— that's enforced for marketplace listing, but it's also a sane operational target.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"So you have","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"four buckets","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Where to store it","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Where to store it","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"INCR","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"EXPIRE","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"plus Lua scripting for true CAS semantics.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Key layout I'd use:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:search:{portalId} ZSET (sliding 1s)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:meta:{portalId} HASH (last seen headers)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The two short-window buckets are sliding-window-log (Redis ZSET, score =","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"microtime","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The daily bucket is a plain","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"INCR","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with TTL set to seconds-until-midnight in","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"HubSpot's account timezone","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to read it","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to read it","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Two-phase: optimistic pre-check, authoritative post-update.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Before a call, run the Lua acquire script. If it returns \"denied,\" sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"After the call, parse","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Max","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Interval-Milliseconds","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and store them in","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:meta:{portalId}","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"For search specifically, since headers don't come back, the local counter","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"is","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"not","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"on 429 (you really did make that request).","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to work with multiple jobs","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to work with multiple jobs","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Concurrency cap","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"via","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Queue::throttle()","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Per-tenant fairness","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Priority lanes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot-priority","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot-bulk","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Backoff on 429","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— respect the","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry-After","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"header. If absent, exponential with jitter (e.g.,","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"min(2^attempt * 250ms, 30s) ± 20%","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to work with paginated requests","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to work with paginated requests","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The trap is treating \"fetch all\" as one logical operation. Each page is its own API call and competes for tokens with everything else.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Two practical rules:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Don't hold the worker idle while paginating.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"processing","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"of page N as a separate job, and queue a \"fetch page N+1\" job with a delay equal to the wait time. Each page becomes its own atomic unit.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Always batch where HubSpot offers it.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/{type}/batch/{read|update|create}","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"accepts up to 100 IDs per call. For your \"patching IDs\" flow this is the difference between 600 calls and 6.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Walkthrough: 600 opportunities","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Walkthrough: 600 opportunities","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Sane flow:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Identify deltas","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— if you can avoid Search at all, do. Use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Batch read","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"current state —","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/deals/batch/read","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with 100 IDs per call → 6 calls. Burst weight 6.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Compute diff","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"in your code (no API).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Batch update","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"—","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/deals/batch/update","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with 100 per call → 6 calls.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Associations","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"—","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v4/associations/{from}/{to}/batch/create","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"if needed → another few batch calls.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Recognition: pre-check denies if","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"<weight>","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"slots aren't free; on 429 the response surfaces","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"policyName","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(DAILY/SECONDLY) — log it, push the job back with","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry-After","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"delay, increment a metric so you can alarm on sustained 429 rate.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Pitfalls & bottlenecks worth pre-empting","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Pitfalls & bottlenecks worth pre-empting","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Daily quota is shared across the whole portal","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search is uniquely fragile","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":": separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hs_object_id","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"ranges or by","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"windows.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry storms after HubSpot incidents","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Webhook → sync → webhook loops","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"jy_last_sync_id","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":") and ignore webhooks where it just changed.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Token refresh","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"expires_in - 60s","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", single-flight the refresh (only one worker refreshes; others wait on a lock).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search latency for fresh writes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Clock skew","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— Redis is authoritative. Don't use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"time()","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"from each worker for window scoring; use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"redis.call('TIME')","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"inside the Lua script.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Dead workers with held tokens","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The 5% error rule","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Phantom contention with Laravel","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"RateLimited","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"middleware","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":": it's per-job-class by default, not per-portal. If your","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"SyncDealJob","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"UpdateActivityJob","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"HubspotRateLimiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"service that everything routes through.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give positive feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give negative feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"You said: What will happen if there are two different types of rate limits?","depth":20,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"You said: What will happen if there are two different types of rate limits?","depth":21,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"What will happen if there are two different types of rate limits?","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search requests that have 5 requests per second","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"13:40","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Edit","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"Claude responded: They're independent buckets — with one shared bucket on top.","depth":21,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Claude responded: They're independent buckets — with one shared bucket on top.","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Analyzed independent rate limit buckets and their practical interactions","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Analyzed independent rate limit buckets and their practical interactions","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"They're independent buckets — with one shared bucket on top.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"The three buckets and what each call hits","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"The three buckets and what each call hits","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"┌──────────────────┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search request ──→ │ Search: 5/sec │ ──┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"└──────────────────┘ │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"├──→ ┌─────────────────┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"┌──────────────────┐ │ │ Daily: 250k–1M │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │","depth":27,"on_screen":false,"role_description":"text"}]...
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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
Show more
10:42
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Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
Copy
Give positive feedback
Give negative feedback
Retry
You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
Retry
Edit
Copy
Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │...
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Claude
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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
Show more
10:42
Retry
Edit
Copy
Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
Copy
Give positive feedback
Give negative feedback
Retry
You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
Retry
Edit
Copy
Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
Copy to clipboard
php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.
Copy
Give positive feedback
Give negative feedback
Retry
You said: Ok, so what will happen each time I make a request?
You said: Ok, so what will happen each time I make a request?
Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?
13:44
Retry
Edit
Copy
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Analyzed per-request overhead concerns and quantified Redis performance impact
Analyzed per-request overhead concerns and quantified Redis performance impact
Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out. But the overhead is much smaller than it sounds, and importantly, it's nowhere near your bottleneck.
What "two buckets per request" actually costs
What "two buckets per request" actually costs
Each request makes
one round trip to Redis
— not two — because you fold both bucket checks into a single Lua script that runs atomically server-side:
Copy to clipboard
lua
-- KEYS[1] = burst bucket key (or search)
-- KEYS[2] = daily bucket key
-- ARGV[1] = now (ms) ARGV[2] = window_ms
-- ARGV[3] = bucket_max ARGV[4] = daily_max
-- ARGV[5] = request_id ARGV[6] = daily_ttl
-- Trim sliding window
redis
.
call
(
'ZREMRANGEBYSCORE'
,
KEYS
[
1
]
,
0
,
ARGV
[
1
]
-
ARGV
[
2
]
)
local
burst_used
=
redis
.
call
(
'ZCARD'
,
KEYS
[
1
]
)
local
daily_used
=
tonumber
(
redis
.
call
(
'GET'
,
KEYS
[
2
]
)
or
'0'
)
if
burst_used
>=
tonumber
(
ARGV
[
3...
|
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Each page is its own API call and competes for tokens with everything else.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Two practical rules:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Don't hold the worker idle while paginating.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"processing","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"of page N as a separate job, and queue a \"fetch page N+1\" job with a delay equal to the wait time. Each page becomes its own atomic unit.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Always batch where HubSpot offers it.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/{type}/batch/{read|update|create}","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"accepts up to 100 IDs per call. For your \"patching IDs\" flow this is the difference between 600 calls and 6.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Walkthrough: 600 opportunities","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Walkthrough: 600 opportunities","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Sane flow:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Identify deltas","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— if you can avoid Search at all, do. Use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Batch read","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"current state —","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/deals/batch/read","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with 100 IDs per call → 6 calls. Burst weight 6.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Compute diff","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"in your code (no API).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Batch update","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"—","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/deals/batch/update","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with 100 per call → 6 calls.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Associations","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"—","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v4/associations/{from}/{to}/batch/create","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"if needed → another few batch calls.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Recognition: pre-check denies if","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"<weight>","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"slots aren't free; on 429 the response surfaces","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"policyName","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(DAILY/SECONDLY) — log it, push the job back with","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry-After","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"delay, increment a metric so you can alarm on sustained 429 rate.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Pitfalls & bottlenecks worth pre-empting","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Pitfalls & bottlenecks worth pre-empting","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Daily quota is shared across the whole portal","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search is uniquely fragile","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":": separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hs_object_id","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"ranges or by","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"windows.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry storms after HubSpot incidents","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Webhook → sync → webhook loops","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"jy_last_sync_id","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":") and ignore webhooks where it just changed.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Token refresh","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"expires_in - 60s","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", single-flight the refresh (only one worker refreshes; others wait on a lock).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search latency for fresh writes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Clock skew","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— Redis is authoritative. Don't use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"time()","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"from each worker for window scoring; use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"redis.call('TIME')","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"inside the Lua script.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Dead workers with held tokens","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The 5% error rule","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Phantom contention with Laravel","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"RateLimited","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"middleware","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":": it's per-job-class by default, not per-portal. If your","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"SyncDealJob","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"UpdateActivityJob","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"HubspotRateLimiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"service that everything routes through.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give positive feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give negative feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"You said: What will happen if there are two different types of rate limits?","depth":20,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"You said: What will happen if there are two different types of rate limits?","depth":21,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"What will happen if there are two different types of rate limits?","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search requests that have 5 requests per second","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"13:40","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Edit","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"Claude responded: They're independent buckets — with one shared bucket on top.","depth":21,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Claude responded: They're independent buckets — with one shared bucket on top.","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Analyzed independent rate limit buckets and their practical interactions","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Analyzed independent rate limit buckets and their practical interactions","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"They're independent buckets — with one shared bucket on top.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"The three buckets and what each call hits","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"The three buckets and what each call hits","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"┌──────────────────┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search request ──→ │ Search: 5/sec │ ──┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"└──────────────────┘ │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"├──→ ┌─────────────────┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"┌──────────────────┐ │ │ Daily: 250k–1M │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"└──────────────────┘ └─────────────────┘","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"What this means practically","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"What this means practically","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting search secondly limit (5/s):","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Only further","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/search","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting burst limit (190/10s):","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting daily limit:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"So can one type of request block the other?","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"So can one type of request block the other?","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search → Burst:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"No.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Burst → Search:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"No.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Saturating burst leaves Search's 5/s untouched.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Either → Daily:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Yes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", but only by contributing to the same daily counter. It's volume that kills you here, not the type.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"In theory you can sustain ~50 search + ~190 other =","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"~240 calls per 10 seconds","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"indefinitely (until daily runs out), because the two buckets bill independently.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Implementation consequence","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Implementation consequence","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"php","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'search'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// for search endpoints only","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'burst'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// for everything else","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'daily'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// every call, both kinds","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('search')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"+","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('daily')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". A batch update calls","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('burst')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"+","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('daily')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":".","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"One subtle pitfall: when you backoff on a 429,","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"inspect","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"policyName","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"in the response body","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to know which bucket actually fired. If you see","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"SECONDLY","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The other subtlety: the burst response gives you","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give positive feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give negative feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"You said: Ok, so what will happen each time I make a request?","depth":20,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"You said: Ok, so what will happen each time I make a request?","depth":21,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"13:44","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Edit","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.","depth":21,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Analyzed per-request overhead concerns and quantified Redis performance impact","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Analyzed per-request overhead concerns and quantified Redis performance impact","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out. But the overhead is much smaller than it sounds, and importantly, it's nowhere near your bottleneck.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"What \"two buckets per request\" actually costs","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"What \"two buckets per request\" actually costs","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Each request makes","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"one round trip to Redis","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— not two — because you fold both bucket checks into a single Lua script that runs atomically server-side:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"lua","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-- KEYS[1] = burst bucket key (or search)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-- KEYS[2] = daily bucket key","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-- ARGV[1] = now (ms) ARGV[2] = window_ms","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-- ARGV[3] = bucket_max ARGV[4] = daily_max","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-- ARGV[5] = request_id ARGV[6] = daily_ttl","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-- Trim sliding window","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"redis","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":".","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"call","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'ZREMRANGEBYSCORE'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"KEYS","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"[","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"]","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"0","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"ARGV","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"[","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"]","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"ARGV","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"[","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"2","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"]","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"local","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"burst_used","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"redis","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":".","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"call","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'ZCARD'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"KEYS","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"[","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"]","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"local","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"daily_used","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"tonumber","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"redis","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":".","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"call","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'GET'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_descriptio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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
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10:42
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Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
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You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
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Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
Copy to clipboard
php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.
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Retry
You said: Ok, so what will happen each time I make a request?
You said: Ok, so what will happen each time I make a request?
Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?
13:44
Retry
Edit
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Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Analyzed per-request overhead concerns and quantified Redis performance impact
Analyzed per-request overhead concerns and quantified Redis performance impact
Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out. But the overhead is much smaller than it sounds, and importantly, it's nowhere near your bottleneck.
What "two buckets per request" actually costs
What "two buckets per request" actually costs
Each request makes
one round trip to Redis
— not two — because you fold both bucket checks into a single Lua script that runs atomically server-side:
Copy to clipboard
lua
-- KEYS[1] = burst bucket key (or search)
-- KEYS[2] = daily bucket key
-- ARGV[1] = now (ms) ARGV[2] = window_ms
-- ARGV[3] = bucket_max ARGV[4] = daily_max
-- ARGV[5] = request_id ARGV[6] = daily_ttl
-- Trim sliding window
redis
.
call
(
'ZREMRANGEBYSCORE'
,
KEYS
[
1
]
,
0
,
ARGV
[
1
]
-
ARGV
[
2
]
)
local
burst_used
=
redis
.
call
(
'ZCARD'
,
KEYS
[
1
]
)
local
daily_used
=
tonumber
(
redis
.
call
(
'GET'
,
KEYS
[
2
]
)
or
'0'
)
if
burst_used
>=
tonumber
(
ARGV
[
3...
|
2601
|
NULL
|
NULL
|
NULL
|
|
2605
|
110
|
15
|
2026-05-07T11:30:22.362871+00:00
|
/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778153422362_m2.jpg...
|
Claude
|
Claude
|
True
|
NULL
|
monitor_2
|
NULL
|
NULL
|
NULL
|
NULL
|
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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
Show more
10:42
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Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
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Give positive feedback
Give negative feedback
Retry
You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
Retry
Edit
Copy
Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
Copy to clipboard
php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.
Copy
Give positive feedback
Give negative feedback
Retry
You said: Ok, so what will happen each time I make a request?
You said: Ok, so what will happen each time I make a request?
Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?
13:44
Retry
Edit
Copy
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Analyzed per-request overhead concerns and quantified Redis performance impact
Analyzed per-request overhead concerns and quantified Redis performance impact
Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out. But the overhead is much smaller than it sounds, and importantly, it's nowhere near your bottleneck.
What "two buckets per request" actually costs
What "two buckets per request" actually costs
Each request makes
one round trip to Redis
— not two — because you fold both bucket checks into a single Lua script that runs atomically server-side:
Copy to clipboard
lua...
|
[{"role":"AXLink","text":& [{"role":"AXLink","text":"Skip to content","depth":14,"bounds":{"left":0.029587766,"top":0.03830806,"width":0.0003324468,"height":0.0007980846},"on_screen":true,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Skip to content","depth":15,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Click to 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Think carefully about the implementation and potential issue and bottlenecks.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Show more","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"10:42","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Edit","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"Claude responded: Current HubSpot limits (Apr 2026)","depth":21,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Claude responded: Current HubSpot limits (Apr 2026)","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Current HubSpot limits (Apr 2026)","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Current HubSpot limits (Apr 2026)","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". The burst window is per app; the daily quota is per portal.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". That's the limit the 429 in your example is hitting (","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"secondly limit","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"on","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/crm/v3/objects/contact/search","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"). It's a separate bucket from the 190/10s, and critically, search responses do not include the","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-*","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"headers","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— you can't read remaining capacity off the response, you have to track it yourself.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Non-search endpoints return","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Max","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", and","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Interval-Milliseconds","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". The daily headers are not returned for OAuth-authenticated calls","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— that's enforced for marketplace listing, but it's also a sane operational target.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"So you have","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"four buckets","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Where to store it","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Where to store it","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"INCR","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"EXPIRE","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"plus Lua scripting for true CAS semantics.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Key layout I'd use:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:search:{portalId} ZSET (sliding 1s)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:meta:{portalId} HASH (last seen headers)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The two short-window buckets are sliding-window-log (Redis ZSET, score =","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"microtime","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The daily bucket is a plain","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"INCR","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with TTL set to seconds-until-midnight in","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"HubSpot's account timezone","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to read it","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to read it","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Two-phase: optimistic pre-check, authoritative post-update.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Before a call, run the Lua acquire script. If it returns \"denied,\" sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"After the call, parse","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Max","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Interval-Milliseconds","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and store them in","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:meta:{portalId}","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"For search specifically, since headers don't come back, the local counter","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"is","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"not","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"on 429 (you really did make that request).","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to work with multiple jobs","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to work with multiple jobs","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Concurrency cap","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"via","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Queue::throttle()","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Per-tenant fairness","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Priority lanes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot-priority","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot-bulk","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Backoff on 429","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— respect the","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry-After","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"header. If absent, exponential with jitter (e.g.,","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"min(2^attempt * 250ms, 30s) ± 20%","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to work with paginated requests","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to work with paginated requests","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The trap is treating \"fetch all\" as one logical operation. Each page is its own API call and competes for tokens with everything else.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Two practical rules:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Don't hold the worker idle while paginating.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"processing","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"of page N as a separate job, and queue a \"fetch page N+1\" job with a delay equal to the wait time. Each page becomes its own atomic unit.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Always batch where HubSpot offers it.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/{type}/batch/{read|update|create}","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"accepts up to 100 IDs per call. For your \"patching IDs\" flow this is the difference between 600 calls and 6.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Walkthrough: 600 opportunities","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Walkthrough: 600 opportunities","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Sane flow:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Identify deltas","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— if you can avoid Search at all, do. Use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Batch read","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"current state —","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/deals/batch/read","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with 100 IDs per call → 6 calls. Burst weight 6.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Compute diff","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"in your code (no API).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Batch update","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"—","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/deals/batch/update","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with 100 per call → 6 calls.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Associations","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"—","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v4/associations/{from}/{to}/batch/create","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"if needed → another few batch calls.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Recognition: pre-check denies if","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"<weight>","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"slots aren't free; on 429 the response surfaces","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"policyName","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(DAILY/SECONDLY) — log it, push the job back with","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry-After","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"delay, increment a metric so you can alarm on sustained 429 rate.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Pitfalls & bottlenecks worth pre-empting","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Pitfalls & bottlenecks worth pre-empting","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Daily quota is shared across the whole portal","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search is uniquely fragile","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":": separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hs_object_id","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"ranges or by","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"windows.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry storms after HubSpot incidents","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Webhook → sync → webhook loops","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"jy_last_sync_id","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":") and ignore webhooks where it just changed.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Token refresh","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"expires_in - 60s","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", single-flight the refresh (only one worker refreshes; others wait on a lock).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search latency for fresh writes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Clock skew","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— Redis is authoritative. Don't use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"time()","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"from each worker for window scoring; use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"redis.call('TIME')","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"inside the Lua script.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Dead workers with held tokens","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The 5% error rule","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Phantom contention with Laravel","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"RateLimited","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"middleware","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":": it's per-job-class by default, not per-portal. If your","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"SyncDealJob","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"UpdateActivityJob","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"HubspotRateLimiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"service that everything routes through.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give positive feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give negative feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"You said: What will happen if there are two different types of rate limits?","depth":20,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"You said: What will happen if there are two different types of rate limits?","depth":21,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"What will happen if there are two different types of rate limits?","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search requests that have 5 requests per second","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"13:40","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Edit","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"Claude responded: They're independent buckets — with one shared bucket on top.","depth":21,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Claude responded: They're independent buckets — with one shared bucket on top.","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Analyzed independent rate limit buckets and their practical interactions","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Analyzed independent rate limit buckets and their practical interactions","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"They're independent buckets — with one shared bucket on top.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"The three buckets and what each call hits","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"The three buckets and what each call hits","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"┌──────────────────┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search request ──→ │ Search: 5/sec │ ──┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"└──────────────────┘ │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"├──→ ┌─────────────────┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"┌──────────────────┐ │ │ Daily: 250k–1M │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"└──────────────────┘ └─────────────────┘","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"What this means practically","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"What this means practically","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting search secondly limit (5/s):","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Only further","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/search","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting burst limit (190/10s):","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting daily limit:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"So can one type of request block the other?","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"So can one type of request block the other?","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search → Burst:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"No.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Burst → Search:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"No.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Saturating burst leaves Search's 5/s untouched.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Either → Daily:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Yes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", but only by contributing to the same daily counter. It's volume that kills you here, not the type.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"In theory you can sustain ~50 search + ~190 other =","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"~240 calls per 10 seconds","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"indefinitely (until daily runs out), because the two buckets bill independently.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Implementation consequence","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Implementation consequence","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"php","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'search'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// for search endpoints only","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'burst'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// for everything else","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'daily'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// every call, both kinds","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('search')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"+","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('daily')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". A batch update calls","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('burst')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"+","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('daily')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":".","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"One subtle pitfall: when you backoff on a 429,","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"inspect","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"policyName","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"in the response body","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to know which bucket actually fired. If you see","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"SECONDLY","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The other subtlety: the burst response gives you","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give positive feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give negative feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"You said: Ok, so what will happen each time I make a request?","depth":20,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"You said: Ok, so what will happen each time I make a request?","depth":21,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"13:44","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Edit","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.","depth":21,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Analyzed per-request overhead concerns and quantified Redis performance impact","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Analyzed per-request overhead concerns and quantified Redis performance impact","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out. But the overhead is much smaller than it sounds, and importantly, it's nowhere near your bottleneck.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"What \"two buckets per request\" actually costs","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"What \"two buckets per request\" actually costs","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Each request makes","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"one round trip to Redis","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— not two — because you fold both bucket checks into a single Lua script that runs atomically server-side:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"lua","depth":26,"on_screen":false,"role_description":"text"}]...
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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
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10:42
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Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
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You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
Retry
Edit
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Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
Copy to clipboard
php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.
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You said: Ok, so what will happen each time I make a request?
You said: Ok, so what will happen each time I make a request?
Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?
13:44
Retry
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Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Analyzed per-request overhead concerns and quantified Redis performance impact
Analyzed per-request overhead concerns and quantified Redis performance impact
Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out. But the overhead is much smaller than it sounds, and importantly, it's nowhere near your bottleneck.
What "two buckets per request" actually costs
What "two buckets per request" actually costs
Each request makes
one round trip to Redis
— not two — because you fold both bucket checks into a single Lua script that runs atomically server-side:
Copy to clipboard
lua...
|
NULL
|
NULL
|
NULL
|
NULL
|
|
2607
|
110
|
16
|
2026-05-07T11:30:24.522842+00:00
|
/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778153424522_m2.jpg...
|
Claude
|
Claude
|
True
|
NULL
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monitor_2
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NULL
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NULL
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NULL
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NULL
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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
Show more
10:42
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Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
Copy
Give positive feedback
Give negative feedback
Retry
You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
Retry
Edit
Copy
Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
Copy to clipboard
php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.
Copy
Give positive feedback
Give negative feedback
Retry
You said: Ok, so what will happen each time I make a request?
You said: Ok, so what will happen each time I make a request?
Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?
13:44
Retry
Edit
Copy
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Analyzed per-request overhead concerns and quantified Redis performance impact
Analyzed per-request overhead concerns and quantified Redis performance impact
Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out. But the overhead is much smaller than it sounds, and importantly, it's nowhere near your bottleneck.
What "two buckets per request" actually costs
What "two buckets per request" actually costs
Each request makes
one round trip to Redis
— not two — because you fold both bucket checks into a single Lua script that runs atomically server-side:
Copy to clipboard
lua
-- KEYS[1] = burst bucket key (or search)
-- KEYS[2] = daily bucket key
-- ARGV[1] = now (ms) ARGV[2] = window_ms
-- ARGV[3] = bucket_max ARGV[4] = daily_max
-- ARGV[5] = request_id ARGV[6] = daily_ttl
-- Trim sliding window
redis
.
call
(
'ZREMRANGEBYSCORE'
,
KEYS
[
1
]
,
0
,
ARGV
[
1
]
-
ARGV
[
2
]
)
local
burst_used
=
redis
.
call
(
'ZCARD'
,
KEYS
[
1
]
)
local
daily_used
=
tonumber
(
redis
.
call
(
'GET'
,
KEYS
[
2
]
)
or
'0'
)
if
burst_used
>=
tonumber
(
ARGV
[
3
]
)
then
-- Tell caller how long to sleep until oldest entry expires
local
oldest
=
redis
.
call
(
'ZRANGE'
,
KEYS
[
1
]
,
0
,
0
,
'WITHSCORES'
)
return
{
0
,
'BURST'
,
(
oldest
[
2
]
+
ARGV
[
2
]
)
-
ARGV
[
1
]
}
end
if
daily_used
>=
tonumber
(
ARGV
[
4
]
)
then
return
{
0
,
'DAILY'
,...
|
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Pro","depth":15,"bounds":{"left":0.0043218085,"top":0.6943336,"width":0.037898935,"height":0.01915403},"on_screen":true,"role_description":"pop-up button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Get apps and extensions","depth":15,"bounds":{"left":0.08277926,"top":0.6943336,"width":0.007978723,"height":0.01915403},"on_screen":true,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"HubSpot rate limit implementation strategy, rename chat","depth":19,"bounds":{"left":0.043218084,"top":0.02793296,"width":0.09773936,"height":0.022346368},"on_screen":true,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"HubSpot rate limit implementation 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The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"updating activity (engagement, meeting, call, note)","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"searching for a company or contact","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"updating deal","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"patching IDs\nReview what the limit is at the moment in the HubSpot documentation and then think of the solution:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"where to store it","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"how to read it","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"how to work with multiple jobs","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines","depth":25,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"). Example of such a error Client error:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST https://api.hubapi.com/crm/v3/objects/contact/search","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"resulted in a","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"429 Too Many Requests","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"response: {\"status\":\"error\",\"message\":\"You have reached your secondly limit.\",\"errorType\":\"RATE_LIMIT\",\"correlationId\":\"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Show more","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"10:42","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Edit","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"Claude responded: Current HubSpot limits (Apr 2026)","depth":21,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Claude responded: Current HubSpot limits (Apr 2026)","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Current HubSpot limits (Apr 2026)","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Current HubSpot limits (Apr 2026)","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". The burst window is per app; the daily quota is per portal.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". That's the limit the 429 in your example is hitting (","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"secondly limit","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"on","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/crm/v3/objects/contact/search","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"). It's a separate bucket from the 190/10s, and critically, search responses do not include the","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-*","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"headers","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— you can't read remaining capacity off the response, you have to track it yourself.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Non-search endpoints return","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Max","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", and","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Interval-Milliseconds","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". The daily headers are not returned for OAuth-authenticated calls","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— that's enforced for marketplace listing, but it's also a sane operational target.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"So you have","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"four buckets","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Where to store it","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Where to store it","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"INCR","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"EXPIRE","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"plus Lua scripting for true CAS semantics.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Key layout I'd use:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:search:{portalId} ZSET (sliding 1s)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:meta:{portalId} HASH (last seen headers)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The two short-window buckets are sliding-window-log (Redis ZSET, score =","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"microtime","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", member = unique request id). 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Get this once and cache it.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to read it","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to read it","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Two-phase: optimistic pre-check, authoritative post-update.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Before a call, run the Lua acquire script. If it returns \"denied,\" sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"After the call, parse","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Max","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Interval-Milliseconds","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and store them in","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:meta:{portalId}","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"For search specifically, since headers don't come back, the local counter","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"is","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"not","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"on 429 (you really did make that request).","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to work with multiple jobs","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to work with multiple jobs","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Concurrency cap","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"via","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Queue::throttle()","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Per-tenant fairness","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Priority lanes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot-priority","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot-bulk","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Backoff on 429","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— respect the","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry-After","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"header. 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Each page is its own API call and competes for tokens with everything else.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Two practical rules:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Don't hold the worker idle while paginating.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"processing","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"of page N as a separate job, and queue a \"fetch page N+1\" job with a delay equal to the wait time. Each page becomes its own atomic unit.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Always batch where HubSpot offers it.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/{type}/batch/{read|update|create}","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"accepts up to 100 IDs per call. For your \"patching IDs\" flow this is the difference between 600 calls and 6.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Walkthrough: 600 opportunities","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Walkthrough: 600 opportunities","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Sane flow:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Identify deltas","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— if you can avoid Search at all, do. Use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Batch read","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"current state —","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/deals/batch/read","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with 100 IDs per call → 6 calls. Burst weight 6.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Compute diff","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"in your code (no API).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Batch update","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"—","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/deals/batch/update","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with 100 per call → 6 calls.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Associations","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"—","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v4/associations/{from}/{to}/batch/create","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"if needed → another few batch calls.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Recognition: pre-check denies if","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"<weight>","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"slots aren't free; on 429 the response surfaces","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"policyName","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(DAILY/SECONDLY) — log it, push the job back with","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry-After","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"delay, increment a metric so you can alarm on sustained 429 rate.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Pitfalls & bottlenecks worth pre-empting","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Pitfalls & bottlenecks worth pre-empting","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Daily quota is shared across the whole portal","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search is uniquely fragile","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":": separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hs_object_id","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"ranges or by","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"windows.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry storms after HubSpot incidents","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Webhook → sync → webhook loops","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"jy_last_sync_id","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":") and ignore webhooks where it just changed.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Token refresh","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"expires_in - 60s","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", single-flight the refresh (only one worker refreshes; others wait on a lock).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search latency for fresh writes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Clock skew","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— Redis is authoritative. Don't use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"time()","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"from each worker for window scoring; use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"redis.call('TIME')","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"inside the Lua script.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Dead workers with held tokens","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The 5% error rule","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Phantom contention with Laravel","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"RateLimited","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"middleware","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":": it's per-job-class by default, not per-portal. If your","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"SyncDealJob","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"UpdateActivityJob","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"HubspotRateLimiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"service that everything routes through.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give positive feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give negative feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"You said: What will happen if there are two different types of rate limits?","depth":20,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"You said: What will happen if there are two different types of rate limits?","depth":21,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"What will happen if there are two different types of rate limits?","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search requests that have 5 requests per second","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"13:40","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Edit","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"Claude responded: They're independent buckets — with one shared bucket on top.","depth":21,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Claude responded: They're independent buckets — with one shared bucket on top.","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Analyzed independent rate limit buckets and their practical interactions","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Analyzed independent rate limit buckets and their practical interactions","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"They're independent buckets — with one shared bucket on top.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"The three buckets and what each call hits","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"The three buckets and what each call hits","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"┌──────────────────┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search request ──→ │ Search: 5/sec │ ──┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"└──────────────────┘ │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"├──→ ┌─────────────────┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"┌──────────────────┐ │ │ Daily: 250k–1M │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"└──────────────────┘ └─────────────────┘","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"What this means practically","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"What this means practically","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting search secondly limit (5/s):","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Only further","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/search","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting burst limit (190/10s):","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting daily limit:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"So can one type of request block the other?","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"So can one type of request block the other?","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search → Burst:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"No.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Burst → Search:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"No.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Saturating burst leaves Search's 5/s untouched.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Either → Daily:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Yes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", but only by contributing to the same daily counter. It's volume that kills you here, not the type.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"In theory you can sustain ~50 search + ~190 other =","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"~240 calls per 10 seconds","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"indefinitely (until daily runs out), because the two buckets bill independently.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Implementation consequence","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Implementation consequence","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"php","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'search'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// for search endpoints only","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'burst'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// for everything else","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'daily'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// every call, both kinds","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('search')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"+","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('daily')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". A batch update calls","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('burst')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"+","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('daily')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":".","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"One subtle pitfall: when you backoff on a 429,","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"inspect","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"policyName","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"in the response body","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to know which bucket actually fired. If you see","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"SECONDLY","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The other subtlety: the burst response gives you","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give positive feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give negative feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"You said: Ok, so what will happen each time I make a request?","depth":20,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"You said: Ok, so what will happen each time I make a request?","depth":21,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"13:44","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Edit","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.","depth":21,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Analyzed per-request overhead concerns and quantified Redis performance impact","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Analyzed per-request overhead concerns and quantified Redis performance impact","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out. But the overhead is much smaller than it sounds, and importantly, it's nowhere near your bottleneck.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"What \"two buckets per request\" actually costs","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"What \"two buckets per request\" actually costs","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Each request makes","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"one round trip to Redis","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— not two — because you fold both bucket checks into a single Lua script that runs atomically server-side:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"lua","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-- KEYS[1] = burst bucket key (or search)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-- KEYS[2] = daily bucket key","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-- ARGV[1] = now (ms) ARGV[2] = window_ms","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-- ARGV[3] = bucket_max ARGV[4] = daily_max","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-- ARGV[5] = request_id ARGV[6] = daily_ttl","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-- Trim sliding 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Tell caller how long to sleep until oldest entry 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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
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10:42
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Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
Copy
Give positive feedback
Give negative feedback
Retry
You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
Retry
Edit
Copy
Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
Copy to clipboard
php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.
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Give negative feedback
Retry
You said: Ok, so what will happen each time I make a request?
You said: Ok, so what will happen each time I make a request?
Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?
13:44
Retry
Edit
Copy
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Analyzed per-request overhead concerns and quantified Redis performance impact
Analyzed per-request overhead concerns and quantified Redis performance impact
Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out. But the overhead is much smaller than it sounds, and importantly, it's nowhere near your bottleneck.
What "two buckets per request" actually costs
What "two buckets per request" actually costs
Each request makes
one round trip to Redis
— not two — because you fold both bucket checks into a single Lua script that runs atomically server-side:
Copy to clipboard
lua
-- KEYS[1] = burst bucket key (or search)
-- KEYS[2] = daily bucket key
-- ARGV[1] = now (ms) ARGV[2] = window_ms
-- ARGV[3] = bucket_max ARGV[4] = daily_max
-- ARGV[5] = request_id ARGV[6] = daily_ttl
-- Trim sliding window
redis
.
call
(
'ZREMRANGEBYSCORE'
,
KEYS
[
1
]
,
0
,
ARGV
[
1
]
-
ARGV
[
2
]
)
local
burst_used
=
redis
.
call
(
'ZCARD'
,
KEYS
[
1
]
)
local
daily_used
=
tonumber
(
redis
.
call
(
'GET'
,
KEYS
[
2
]
)
or
'0'
)
if
burst_used
>=
tonumber
(
ARGV
[
3
]
)
then
-- Tell caller how long to sleep until oldest entry expires
local
oldest
=
redis
.
call
(
'ZRANGE'
,
KEYS
[
1
]
,
0
,
0
,
'WITHSCORES'
)
return
{
0
,
'BURST'
,
(
oldest
[
2
]
+
ARGV
[
2
]
)
-
ARGV
[
1
]
}
end
if
daily_used
>=
tonumber
(
ARGV
[
4
]
)
then
return
{
0
,
'DAILY'
,...
|
2605
|
NULL
|
NULL
|
NULL
|
|
2608
|
110
|
17
|
2026-05-07T11:30:36.668996+00:00
|
/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778153436668_m2.jpg...
|
Claude
|
Claude
|
True
|
NULL
|
monitor_2
|
NULL
|
NULL
|
NULL
|
NULL
|
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Claude is responding
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
Show more
10:42
Edit
Copy
Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
Copy
Give positive feedback
Give negative feedback
You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
Edit
Copy
Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
Copy to clipboard
php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.
Copy
Give positive feedback
Give negative feedback
You said: Ok, so what will happen each time I make a request?
You said: Ok, so what will happen each time I make a request?
Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?
13:44
Edit
Copy
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Analyzed per-request overhead concerns and quantified Redis performance impact
Analyzed per-request overhead concerns and quantified Redis performance impact
Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out. But the overhead is much smaller than it sounds, and importantly, it's nowhere near your bottleneck.
What "two buckets per request" actually costs
What "two buckets per request" actually costs
Each request makes
one round trip to Redis
— not two — because you fold both bucket checks into a single Lua script that runs atomically server-side:
Copy to clipboard
lua
-- KEYS[1] = burst bucket key (or search)
-- KEYS[2] = daily bucket key
-- ARGV[1] = now (ms) ARGV[2] = window_ms
-- ARGV[3] = bucket_max ARGV[4] = daily_max
-- ARGV[5] = request_id ARGV[6] = daily_ttl
-- Trim sliding window
redis
.
call
(
'ZREMRANGEBYSCORE'
,
KEYS
[
1
]
,
0
,
ARGV
[
1
]
-
ARGV
[
2
]
)
local
burst_used
=
redis
.
call
(
'ZCARD'
,
KEYS
[
1
]
)
local
daily_used
=
tonumber
(
redis
.
call
(
'GET'
,
KEYS
[
2
]
)
or...
|
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sidebar","depth":21,"bounds":{"left":0.48537233,"top":0.026336791,"width":0.010638298,"height":0.025538707},"on_screen":true,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Claude is responding","depth":21,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"You said: So just a solution for rate limit implementation.","depth":20,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"You said: So just a solution for rate limit implementation.","depth":21,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"updating activity (engagement, meeting, call, note)","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"searching for a company or contact","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"updating deal","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"patching IDs\nReview what the limit is at the moment in the HubSpot documentation and then think of the solution:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"where to store it","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"how to read it","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"how to work with multiple jobs","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines","depth":25,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"). Example of such a error Client error:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST https://api.hubapi.com/crm/v3/objects/contact/search","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"resulted in a","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"429 Too Many Requests","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"response: {\"status\":\"error\",\"message\":\"You have reached your secondly limit.\",\"errorType\":\"RATE_LIMIT\",\"correlationId\":\"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Show more","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"10:42","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Edit","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"Claude responded: Current HubSpot limits (Apr 2026)","depth":21,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Claude responded: Current HubSpot limits (Apr 2026)","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Current HubSpot limits (Apr 2026)","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Current HubSpot limits (Apr 2026)","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". The burst window is per app; the daily quota is per portal.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". That's the limit the 429 in your example is hitting (","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"secondly limit","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"on","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/crm/v3/objects/contact/search","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"). It's a separate bucket from the 190/10s, and critically, search responses do not include the","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-*","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"headers","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— you can't read remaining capacity off the response, you have to track it yourself.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Non-search endpoints return","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Max","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", and","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Interval-Milliseconds","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". The daily headers are not returned for OAuth-authenticated calls","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— that's enforced for marketplace listing, but it's also a sane operational target.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"So you have","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"four buckets","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Where to store it","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Where to store it","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"INCR","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"EXPIRE","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"plus Lua scripting for true CAS semantics.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Key layout I'd use:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:search:{portalId} ZSET (sliding 1s)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:meta:{portalId} HASH (last seen headers)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The two short-window buckets are sliding-window-log (Redis ZSET, score =","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"microtime","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The daily bucket is a plain","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"INCR","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with TTL set to seconds-until-midnight in","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"HubSpot's account timezone","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to read it","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to read it","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Two-phase: optimistic pre-check, authoritative post-update.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Before a call, run the Lua acquire script. If it returns \"denied,\" sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"After the call, parse","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Max","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Interval-Milliseconds","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and store them in","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:meta:{portalId}","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"For search specifically, since headers don't come back, the local counter","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"is","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"not","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"on 429 (you really did make that request).","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to work with multiple jobs","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to work with multiple jobs","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Concurrency cap","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"via","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Queue::throttle()","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Per-tenant fairness","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Priority lanes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot-priority","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot-bulk","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Backoff on 429","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— respect the","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry-After","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"header. If absent, exponential with jitter (e.g.,","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"min(2^attempt * 250ms, 30s) ± 20%","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to work with paginated requests","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to work with paginated requests","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The trap is treating \"fetch all\" as one logical operation. Each page is its own API call and competes for tokens with everything else.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Two practical rules:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Don't hold the worker idle while paginating.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"processing","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"of page N as a separate job, and queue a \"fetch page N+1\" job with a delay equal to the wait time. Each page becomes its own atomic unit.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Always batch where HubSpot offers it.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/{type}/batch/{read|update|create}","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"accepts up to 100 IDs per call. For your \"patching IDs\" flow this is the difference between 600 calls and 6.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Walkthrough: 600 opportunities","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Walkthrough: 600 opportunities","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Sane flow:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Identify deltas","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— if you can avoid Search at all, do. Use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Batch read","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"current state —","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/deals/batch/read","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with 100 IDs per call → 6 calls. Burst weight 6.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Compute diff","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"in your code (no API).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Batch update","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"—","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/deals/batch/update","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with 100 per call → 6 calls.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Associations","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"—","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v4/associations/{from}/{to}/batch/create","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"if needed → another few batch calls.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Recognition: pre-check denies if","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"<weight>","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"slots aren't free; on 429 the response surfaces","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"policyName","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(DAILY/SECONDLY) — log it, push the job back with","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry-After","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"delay, increment a metric so you can alarm on sustained 429 rate.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Pitfalls & bottlenecks worth pre-empting","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Pitfalls & bottlenecks worth pre-empting","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Daily quota is shared across the whole portal","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search is uniquely fragile","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":": separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hs_object_id","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"ranges or by","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"windows.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry storms after HubSpot incidents","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Webhook → sync → webhook loops","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"jy_last_sync_id","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":") and ignore webhooks where it just changed.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Token refresh","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"expires_in - 60s","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", single-flight the refresh (only one worker refreshes; others wait on a lock).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search latency for fresh writes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Clock skew","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— Redis is authoritative. Don't use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"time()","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"from each worker for window scoring; use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"redis.call('TIME')","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"inside the Lua script.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Dead workers with held tokens","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The 5% error rule","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Phantom contention with Laravel","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"RateLimited","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"middleware","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":": it's per-job-class by default, not per-portal. If your","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"SyncDealJob","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"UpdateActivityJob","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"HubspotRateLimiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"service that everything routes through.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give positive feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give negative feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"You said: What will happen if there are two different types of rate limits?","depth":20,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"You said: What will happen if there are two different types of rate limits?","depth":21,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"What will happen if there are two different types of rate limits?","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search requests that have 5 requests per second","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"13:40","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Edit","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"Claude responded: They're independent buckets — with one shared bucket on top.","depth":21,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Claude responded: They're independent buckets — with one shared bucket on top.","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Analyzed independent rate limit buckets and their practical interactions","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Analyzed independent rate limit buckets and their practical interactions","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"They're independent buckets — with one shared bucket on top.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"The three buckets and what each call hits","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"The three buckets and what each call hits","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"┌──────────────────┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search request ──→ │ Search: 5/sec │ ──┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"└──────────────────┘ │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"├──→ ┌─────────────────┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"┌──────────────────┐ │ │ Daily: 250k–1M │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"└──────────────────┘ └─────────────────┘","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"What this means practically","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"What this means practically","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting search secondly limit (5/s):","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Only further","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/search","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting burst limit (190/10s):","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting daily limit:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"So can one type of request block the other?","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"So can one type of request block the other?","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search → Burst:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"No.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Burst → Search:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"No.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Saturating burst leaves Search's 5/s untouched.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Either → Daily:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Yes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", but only by contributing to the same daily counter. It's volume that kills you here, not the type.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"In theory you can sustain ~50 search + ~190 other =","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"~240 calls per 10 seconds","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"indefinitely (until daily runs out), because the two buckets bill independently.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Implementation consequence","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Implementation consequence","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"php","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'search'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// for search endpoints only","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'burst'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// for everything else","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'daily'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// every call, both kinds","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('search')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"+","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('daily')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". A batch update calls","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('burst')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"+","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('daily')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":".","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"One subtle pitfall: when you backoff on a 429,","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"inspect","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"policyName","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"in the response body","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to know which bucket actually fired. If you see","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"SECONDLY","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The other subtlety: the burst response gives you","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give positive feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give negative feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"You said: Ok, so what will happen each time I make a request?","depth":20,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"You said: Ok, so what will happen each time I make a request?","depth":21,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"13:44","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Edit","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.","depth":21,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Analyzed per-request overhead concerns and quantified Redis performance impact","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Analyzed per-request overhead concerns and quantified Redis performance impact","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out. But the overhead is much smaller than it sounds, and importantly, it's nowhere near your bottleneck.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"What \"two buckets per request\" actually costs","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"What \"two buckets per request\" actually costs","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Each request makes","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"one round trip to Redis","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— not two — because you fold both bucket checks into a single Lua script that runs atomically server-side:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"lua","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-- KEYS[1] = burst bucket key (or search)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-- KEYS[2] = daily bucket key","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-- ARGV[1] = now (ms) ARGV[2] = window_ms","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-- ARGV[3] = bucket_max ARGV[4] = daily_max","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-- ARGV[5] = request_id ARGV[6] = daily_ttl","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-- Trim sliding window","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"redis","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":".","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"call","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'ZREMRANGEBYSCORE'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"KEYS","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"[","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"]","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"0","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"ARGV","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"[","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"]","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"ARGV","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"[","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"2","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"]","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"local","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"burst_used","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"redis","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":".","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"call","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'ZCARD'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"KEYS","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"[","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"]","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"local","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"daily_used","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"tonumber","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"redis","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":".","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"call","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'GET'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"KEYS","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"[","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"2","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"]","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"or","depth":27,"on_screen":false,"role_description":"text"}]...
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HubSpot rate limit implementation strategy
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Claude is responding
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
Show more
10:42
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Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
Copy
Give positive feedback
Give negative feedback
You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
Edit
Copy
Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
Copy to clipboard
php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.
Copy
Give positive feedback
Give negative feedback
You said: Ok, so what will happen each time I make a request?
You said: Ok, so what will happen each time I make a request?
Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?
13:44
Edit
Copy
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Analyzed per-request overhead concerns and quantified Redis performance impact
Analyzed per-request overhead concerns and quantified Redis performance impact
Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out. But the overhead is much smaller than it sounds, and importantly, it's nowhere near your bottleneck.
What "two buckets per request" actually costs
What "two buckets per request" actually costs
Each request makes
one round trip to Redis
— not two — because you fold both bucket checks into a single Lua script that runs atomically server-side:
Copy to clipboard
lua
-- KEYS[1] = burst bucket key (or search)
-- KEYS[2] = daily bucket key
-- ARGV[1] = now (ms) ARGV[2] = window_ms
-- ARGV[3] = bucket_max ARGV[4] = daily_max
-- ARGV[5] = request_id ARGV[6] = daily_ttl
-- Trim sliding window
redis
.
call
(
'ZREMRANGEBYSCORE'
,
KEYS
[
1
]
,
0
,
ARGV
[
1
]
-
ARGV
[
2
]
)
local
burst_used
=
redis
.
call
(
'ZCARD'
,
KEYS
[
1
]
)
local
daily_used
=
tonumber
(
redis
.
call
(
'GET'
,
KEYS
[
2
]
)
or...
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18
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2026-05-07T11:31:07.187194+00:00
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/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778153467187_m2.jpg...
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Claude
|
Claude
|
True
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NULL
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monitor_2
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NULL
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NULL
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NULL
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NULL
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HubSpot rate limit implementation strategy, rename chat
HubSpot rate limit implementation strategy
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Claude is responding
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
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10:42
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Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
Copy
Give positive feedback
Give negative feedback
You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
Edit
Copy
Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
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php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.
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Give positive feedback
Give negative feedback
You said: Ok, so what will happen each time I make a request?
You said: Ok, so what will happen each time I make a request?
Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?
13:44
Edit
Copy
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Analyzed per-request overhead concerns and quantified Redis performance impact
Analyzed per-request overhead concerns and quantified Redis performance impact
Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out. But the overhead is much smaller than it sounds, and importantly, it's nowhere near your bottleneck.
What "two buckets per request" actually costs
What "two buckets per request" actually costs
Each request makes
one round trip to Redis
— not two — because you fold both bucket checks into a single Lua script that runs atomically server-side:
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lua
-- KEYS[1] = burst bucket key (or search)
-- KEYS[2] = daily bucket key
-- ARGV[1] = now (ms) ARGV[2] = window_ms
-- ARGV[3] = bucket_max ARGV[4] = daily_max
-- ARGV[5] = request_id ARGV[6] = daily_ttl
-- Trim sliding window
redis
.
call
(
'ZREMRANGEBYSCORE'
,
KEYS
[
1
]
,
0
,
ARGV
[
1
]
-
ARGV
[
2
]
)
local
burst_used
=
redis
.
call
(
'ZCARD'
,
KEYS
[
1
]
)
local
daily_used
=
tonumber
(
redis
.
call
(
'GET'
,
KEYS
[
2
]
)
or
'0'
)
if
burst_used
>=
tonumber
(
ARGV
[
3
]
)
then
-- Tell caller how long to sleep until oldest entry expires
local
oldest
=
redis
.
call
(
'ZRANGE'
,
KEYS
[
1
]
,
0
,
0
,
'WITHSCORES'
)
return
{
0
,
'BURST'
,
(
oldest
[
2
]
+
ARGV
[
2
]
)
-
ARGV
[
1
]
}
end
if
daily_used
>=
tonumber
(
ARGV
[
4
]...
|
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Laravel already speaks Redis natively, and you get atomic","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"INCR","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"EXPIRE","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"plus Lua scripting for true CAS semantics.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Key layout I'd use:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:search:{portalId} ZSET (sliding 1s)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:meta:{portalId} HASH (last seen headers)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The two short-window buckets are sliding-window-log (Redis ZSET, score =","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"microtime","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The daily bucket is a plain","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"INCR","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with TTL set to seconds-until-midnight in","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"HubSpot's account timezone","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to read it","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to read it","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Two-phase: optimistic pre-check, authoritative post-update.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Before a call, run the Lua acquire script. If it returns \"denied,\" sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"After the call, parse","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Max","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Interval-Milliseconds","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and store them in","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:meta:{portalId}","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"For search specifically, since headers don't come back, the local counter","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"is","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"not","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"on 429 (you really did make that request).","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to work with multiple jobs","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to work with multiple jobs","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Concurrency cap","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"via","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Queue::throttle()","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Per-tenant fairness","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Priority lanes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot-priority","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot-bulk","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Backoff on 429","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— respect the","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry-After","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"header. If absent, exponential with jitter (e.g.,","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"min(2^attempt * 250ms, 30s) ± 20%","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to work with paginated requests","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to work with paginated requests","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The trap is treating \"fetch all\" as one logical operation. Each page is its own API call and competes for tokens with everything else.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Two practical rules:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Don't hold the worker idle while paginating.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"processing","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"of page N as a separate job, and queue a \"fetch page N+1\" job with a delay equal to the wait time. Each page becomes its own atomic unit.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Always batch where HubSpot offers it.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/{type}/batch/{read|update|create}","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"accepts up to 100 IDs per call. For your \"patching IDs\" flow this is the difference between 600 calls and 6.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Walkthrough: 600 opportunities","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Walkthrough: 600 opportunities","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Sane flow:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Identify deltas","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— if you can avoid Search at all, do. Use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Batch read","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"current state —","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/deals/batch/read","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with 100 IDs per call → 6 calls. Burst weight 6.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Compute diff","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"in your code (no API).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Batch update","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"—","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/deals/batch/update","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with 100 per call → 6 calls.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Associations","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"—","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v4/associations/{from}/{to}/batch/create","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"if needed → another few batch calls.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Recognition: pre-check denies if","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"<weight>","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"slots aren't free; on 429 the response surfaces","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"policyName","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(DAILY/SECONDLY) — log it, push the job back with","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry-After","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"delay, increment a metric so you can alarm on sustained 429 rate.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Pitfalls & bottlenecks worth pre-empting","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Pitfalls & bottlenecks worth pre-empting","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Daily quota is shared across the whole portal","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search is uniquely fragile","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":": separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hs_object_id","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"ranges or by","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"windows.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry storms after HubSpot incidents","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Webhook → sync → webhook loops","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"jy_last_sync_id","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":") and ignore webhooks where it just changed.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Token refresh","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"expires_in - 60s","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", single-flight the refresh (only one worker refreshes; others wait on a lock).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search latency for fresh writes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Clock skew","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— Redis is authoritative. Don't use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"time()","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"from each worker for window scoring; use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"redis.call('TIME')","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"inside the Lua script.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Dead workers with held tokens","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The 5% error rule","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Phantom contention with Laravel","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"RateLimited","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"middleware","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":": it's per-job-class by default, not per-portal. If your","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"SyncDealJob","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"UpdateActivityJob","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"HubspotRateLimiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"service that everything routes through.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give positive feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give negative feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"You said: What will happen if there are two different types of rate limits?","depth":20,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"You said: What will happen if there are two different types of rate limits?","depth":21,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"What will happen if there are two different types of rate limits?","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search requests that have 5 requests per second","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"13:40","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Edit","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"Claude responded: They're independent buckets — with one shared bucket on top.","depth":21,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Claude responded: They're independent buckets — with one shared bucket on top.","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Analyzed independent rate limit buckets and their practical interactions","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Analyzed independent rate limit buckets and their practical interactions","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"They're independent buckets — with one shared bucket on top.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"The three buckets and what each call hits","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"The three buckets and what each call hits","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"┌──────────────────┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search request ──→ │ Search: 5/sec │ ──┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"└──────────────────┘ │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"├──→ ┌─────────────────┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"┌──────────────────┐ │ │ Daily: 250k–1M │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"└──────────────────┘ └─────────────────┘","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"What this means practically","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"What this means practically","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting search secondly limit (5/s):","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Only further","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/search","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting burst limit (190/10s):","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting daily limit:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"So can one type of request block the other?","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"So can one type of request block the other?","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search → Burst:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"No.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Burst → Search:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"No.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Saturating burst leaves Search's 5/s untouched.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Either → Daily:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Yes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", but only by contributing to the same daily counter. 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":"}","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"end","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"if","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"daily_used","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":">=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"tonumber","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"ARGV","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"[","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"4","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"]","depth":27,"on_screen":false,"role_description":"text"}]...
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HubSpot rate limit implementation strategy
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Claude is responding
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
Show more
10:42
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Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
Copy
Give positive feedback
Give negative feedback
You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
Edit
Copy
Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
Copy to clipboard
php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.
Copy
Give positive feedback
Give negative feedback
You said: Ok, so what will happen each time I make a request?
You said: Ok, so what will happen each time I make a request?
Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?
13:44
Edit
Copy
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Analyzed per-request overhead concerns and quantified Redis performance impact
Analyzed per-request overhead concerns and quantified Redis performance impact
Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out. But the overhead is much smaller than it sounds, and importantly, it's nowhere near your bottleneck.
What "two buckets per request" actually costs
What "two buckets per request" actually costs
Each request makes
one round trip to Redis
— not two — because you fold both bucket checks into a single Lua script that runs atomically server-side:
Copy to clipboard
lua
-- KEYS[1] = burst bucket key (or search)
-- KEYS[2] = daily bucket key
-- ARGV[1] = now (ms) ARGV[2] = window_ms
-- ARGV[3] = bucket_max ARGV[4] = daily_max
-- ARGV[5] = request_id ARGV[6] = daily_ttl
-- Trim sliding window
redis
.
call
(
'ZREMRANGEBYSCORE'
,
KEYS
[
1
]
,
0
,
ARGV
[
1
]
-
ARGV
[
2
]
)
local
burst_used
=
redis
.
call
(
'ZCARD'
,
KEYS
[
1
]
)
local
daily_used
=
tonumber
(
redis
.
call
(
'GET'
,
KEYS
[
2
]
)
or
'0'
)
if
burst_used
>=
tonumber
(
ARGV
[
3
]
)
then
-- Tell caller how long to sleep until oldest entry expires
local
oldest
=
redis
.
call
(
'ZRANGE'
,
KEYS
[
1
]
,
0
,
0
,
'WITHSCORES'
)
return
{
0
,
'BURST'
,
(
oldest
[
2
]
+
ARGV
[
2
]
)
-
ARGV
[
1
]
}
end
if
daily_used
>=
tonumber
(
ARGV
[
4
]...
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2608
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2026-05-07T11:31:37.684685+00:00
|
/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778153497684_m2.jpg...
|
Claude
|
Claude
|
True
|
NULL
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monitor_2
|
NULL
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NULL
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NULL
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NULL
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HubSpot rate limit implementation strategy
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HubSpot rate limit implementation strategy, rename chat
HubSpot rate limit implementation strategy
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Claude is responding
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
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10:42
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Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
Copy
Give positive feedback
Give negative feedback
You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
Edit
Copy
Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
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php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.
Copy
Give positive feedback
Give negative feedback
You said: Ok, so what will happen each time I make a request?
You said: Ok, so what will happen each time I make a request?
Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?
13:44
Edit
Copy
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Analyzed per-request overhead concerns and quantified Redis performance impact
Analyzed per-request overhead concerns and quantified Redis performance impact
Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out. But the overhead is much smaller than it sounds, and importantly, it's nowhere near your bottleneck.
What "two buckets per request" actually costs
What "two buckets per request" actually costs
Each request makes
one round trip to Redis
— not two — because you fold both bucket checks into a single Lua script that runs atomically server-side:
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lua
-- KEYS[1] = burst bucket key (or search)
-- KEYS[2] = daily bucket key
-- ARGV[1] = now (ms) ARGV[2] = window_ms
-- ARGV[3] = bucket_max ARGV[4] = daily_max
-- ARGV[5] = request_id ARGV[6] = daily_ttl
-- Trim sliding window
redis
.
call
(
'ZREMRANGEBYSCORE'
,
KEYS
[
1
]
,
0
,
ARGV
[
1
]
-
ARGV
[
2
]
)
local
burst_used
=
redis
.
call
(
'ZCARD'
,
KEYS
[
1
]
)
local
daily_used
=
tonumber
(
redis
.
call
(
'GET'
,
KEYS
[
2
]
)
or
'0'
)
if
burst_used
>=
tonumber
(
ARGV
[
3
]
)
then
-- Tell caller how long to sleep until oldest entry expires
local
oldest
=
redis
.
call
(
'ZRANGE'
,
KEYS
[
1
]
,
0
,
0
,
'WITHSCORES'
)
return
{
0
,
'BURST'
,
(
oldest
[
2
]
+
ARGV
[
2
]
)
-
ARGV
[
1
]
}
end
if
daily_used
>=
tonumber
(
ARGV
[
4
]
)
then
return
{
0
,
'DAILY'
,
-
1
}
end
redis
.
call
(
'ZADD'
,
KEYS
[
1
]
,
ARGV
[
1
]
,
ARGV
[
5
]
)
redis
.
call
(
'PEXPIRE'
,
KEYS
[
1
]
,
ARGV
[
2
]
+
1000
)
local
d
=
redis
.
call
(
'INCR'
,
KEYS
[
2
]
)
if
d
==...
|
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chat ⌘N","depth":15,"bounds":{"left":0.0043218085,"top":0.08938547,"width":0.08643617,"height":0.0207502},"on_screen":true,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"New chat","depth":16,"bounds":{"left":0.014295213,"top":0.0933759,"width":0.018949468,"height":0.012769354},"on_screen":true,"lines":[{"char_start":0,"char_count":1,"bounds":{"left":0.014295213,"top":0.0933759,"width":0.003656915,"height":0.013567438}},{"char_start":1,"char_count":7,"bounds":{"left":0.01761968,"top":0.0933759,"width":0.015957447,"height":0.013567438}}],"role_description":"text"},{"role":"AXStaticText","text":"⌘N","depth":17,"bounds":{"left":0.08178192,"top":0.0933759,"width":0.006981383,"height":0.012769354},"on_screen":true,"role_description":"text"},{"role":"AXButton","text":"Projects","depth":15,"bounds":{"left":0.0043218085,"top":0.110135674,"width":0.08643617,"height":0.019952115},"on_screen":true,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Artifacts","depth":15,"bounds":{"left":0.0043218085,"top":0.1300878,"width":0.08643617,"height":0.0207502},"on_screen":true,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Customize","depth":15,"bounds":{"left":0.0043218085,"top":0.15003991,"width":0.08643617,"height":0.0207502},"on_screen":true,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Pinned","depth":16,"bounds":{"left":0.0063164895,"top":0.18914606,"width":0.08377659,"height":0.013567438},"on_screen":true,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":true},{"role":"AXButton","text":"Bulgarian 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Laravel already speaks Redis natively, and you get atomic","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"INCR","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"EXPIRE","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"plus Lua scripting for true CAS semantics.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Key layout I'd use:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:search:{portalId} ZSET (sliding 1s)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:meta:{portalId} HASH (last seen headers)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The two short-window buckets are sliding-window-log (Redis ZSET, score =","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"microtime","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The daily bucket is a plain","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"INCR","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with TTL set to seconds-until-midnight in","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"HubSpot's account timezone","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to read it","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to read it","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Two-phase: optimistic pre-check, authoritative post-update.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Before a call, run the Lua acquire script. If it returns \"denied,\" sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"After the call, parse","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Max","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Interval-Milliseconds","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and store them in","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:meta:{portalId}","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"For search specifically, since headers don't come back, the local counter","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"is","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"not","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"on 429 (you really did make that request).","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to work with multiple jobs","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to work with multiple jobs","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Concurrency cap","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"via","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Queue::throttle()","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Per-tenant fairness","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Priority lanes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot-priority","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot-bulk","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Backoff on 429","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— respect the","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry-After","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"header. If absent, exponential with jitter (e.g.,","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"min(2^attempt * 250ms, 30s) ± 20%","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to work with paginated requests","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to work with paginated requests","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The trap is treating \"fetch all\" as one logical operation. Each page is its own API call and competes for tokens with everything else.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Two practical rules:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Don't hold the worker idle while paginating.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"processing","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"of page N as a separate job, and queue a \"fetch page N+1\" job with a delay equal to the wait time. Each page becomes its own atomic unit.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Always batch where HubSpot offers it.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/{type}/batch/{read|update|create}","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"accepts up to 100 IDs per call. For your \"patching IDs\" flow this is the difference between 600 calls and 6.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Walkthrough: 600 opportunities","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Walkthrough: 600 opportunities","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Sane flow:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Identify deltas","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— if you can avoid Search at all, do. Use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Batch read","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"current state —","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/deals/batch/read","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with 100 IDs per call → 6 calls. Burst weight 6.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Compute diff","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"in your code (no API).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Batch update","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"—","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/deals/batch/update","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with 100 per call → 6 calls.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Associations","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"—","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v4/associations/{from}/{to}/batch/create","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"if needed → another few batch calls.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Recognition: pre-check denies if","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"<weight>","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"slots aren't free; on 429 the response surfaces","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"policyName","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(DAILY/SECONDLY) — log it, push the job back with","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry-After","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"delay, increment a metric so you can alarm on sustained 429 rate.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Pitfalls & bottlenecks worth pre-empting","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Pitfalls & bottlenecks worth pre-empting","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Daily quota is shared across the whole portal","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search is uniquely fragile","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":": separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hs_object_id","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"ranges or by","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"windows.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry storms after HubSpot incidents","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Webhook → sync → webhook loops","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"jy_last_sync_id","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":") and ignore webhooks where it just changed.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Token refresh","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"expires_in - 60s","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", single-flight the refresh (only one worker refreshes; others wait on a lock).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search latency for fresh writes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Clock skew","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— Redis is authoritative. Don't use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"time()","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"from each worker for window scoring; use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"redis.call('TIME')","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"inside the Lua script.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Dead workers with held tokens","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The 5% error rule","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Phantom contention with Laravel","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"RateLimited","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"middleware","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":": it's per-job-class by default, not per-portal. If your","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"SyncDealJob","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"UpdateActivityJob","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"HubspotRateLimiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"service that everything routes through.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give positive feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give negative feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"You said: What will happen if there are two different types of rate limits?","depth":20,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"You said: What will happen if there are two different types of rate limits?","depth":21,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"What will happen if there are two different types of rate limits?","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search requests that have 5 requests per second","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"13:40","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Edit","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"Claude responded: They're independent buckets — with one shared bucket on top.","depth":21,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Claude responded: They're independent buckets — with one shared bucket on top.","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Analyzed independent rate limit buckets and their practical interactions","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Analyzed independent rate limit buckets and their practical interactions","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"They're independent buckets — with one shared bucket on top.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"The three buckets and what each call hits","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"The three buckets and what each call hits","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"┌──────────────────┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search request ──→ │ Search: 5/sec │ ──┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"└──────────────────┘ │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"├──→ ┌─────────────────┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"┌──────────────────┐ │ │ Daily: 250k–1M │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"└──────────────────┘ └─────────────────┘","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"What this means practically","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"What this means practically","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting search secondly limit (5/s):","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Only further","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/search","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting burst limit (190/10s):","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting daily limit:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"So can one type of request block the other?","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"So can one type of request block the other?","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search → Burst:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"No.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Burst → Search:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"No.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Saturating burst leaves Search's 5/s untouched.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Either → Daily:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Yes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", but only by contributing to the same daily counter. 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Claude is responding
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
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10:42
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Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
Copy
Give positive feedback
Give negative feedback
You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
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Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
Copy to clipboard
php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.
Copy
Give positive feedback
Give negative feedback
You said: Ok, so what will happen each time I make a request?
You said: Ok, so what will happen each time I make a request?
Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?
13:44
Edit
Copy
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Analyzed per-request overhead concerns and quantified Redis performance impact
Analyzed per-request overhead concerns and quantified Redis performance impact
Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out. But the overhead is much smaller than it sounds, and importantly, it's nowhere near your bottleneck.
What "two buckets per request" actually costs
What "two buckets per request" actually costs
Each request makes
one round trip to Redis
— not two — because you fold both bucket checks into a single Lua script that runs atomically server-side:
Copy to clipboard
lua
-- KEYS[1] = burst bucket key (or search)
-- KEYS[2] = daily bucket key
-- ARGV[1] = now (ms) ARGV[2] = window_ms
-- ARGV[3] = bucket_max ARGV[4] = daily_max
-- ARGV[5] = request_id ARGV[6] = daily_ttl
-- Trim sliding window
redis
.
call
(
'ZREMRANGEBYSCORE'
,
KEYS
[
1
]
,
0
,
ARGV
[
1
]
-
ARGV
[
2
]
)
local
burst_used
=
redis
.
call
(
'ZCARD'
,
KEYS
[
1
]
)
local
daily_used
=
tonumber
(
redis
.
call
(
'GET'
,
KEYS
[
2
]
)
or
'0'
)
if
burst_used
>=
tonumber
(
ARGV
[
3
]
)
then
-- Tell caller how long to sleep until oldest entry expires
local
oldest
=
redis
.
call
(
'ZRANGE'
,
KEYS
[
1
]
,
0
,
0
,
'WITHSCORES'
)
return
{
0
,
'BURST'
,
(
oldest
[
2
]
+
ARGV
[
2
]
)
-
ARGV
[
1
]
}
end
if
daily_used
>=
tonumber
(
ARGV
[
4
]
)
then
return
{
0
,
'DAILY'
,
-
1
}
end
redis
.
call
(
'ZADD'
,
KEYS
[
1
]
,
ARGV
[
1
]
,
ARGV
[
5
]
)
redis
.
call
(
'PEXPIRE'
,
KEYS
[
1
]
,
ARGV
[
2
]
+
1000
)
local
d
=
redis
.
call
(
'INCR'
,
KEYS
[
2
]
)
if
d
==...
|
NULL
|
NULL
|
NULL
|
NULL
|
|
2614
|
110
|
20
|
2026-05-07T11:31:58.398388+00:00
|
/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778153518398_m2.jpg...
|
Claude
|
Claude
|
True
|
NULL
|
monitor_2
|
NULL
|
NULL
|
NULL
|
NULL
|
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Close
Claude is responding
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
Show more
10:42
Edit
Copy
Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
Copy
Give positive feedback
Give negative feedback
You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
Edit
Copy
Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
Copy to clipboard
php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.
Copy
Give positive feedback
Give negative feedback
You said: Ok, so what will happen each time I make a request?
You said: Ok, so what will happen each time I make a request?
Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?
13:44
Edit
Copy
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Analyzed per-request overhead concerns and quantified Redis performance impact
Analyzed per-request overhead concerns and quantified Redis performance impact
Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out. But the overhead is much smaller than it sounds, and importantly, it's nowhere near your bottleneck.
What "two buckets per request" actually costs
What "two buckets per request" actually costs
Each request makes
one round trip to Redis
— not two — because you fold both bucket checks into a single Lua script that runs atomically server-side:
Copy to clipboard
lua
-- KEYS[1] = burst bucket key (or search)
-- KEYS[2] = daily bucket key
-- ARGV[1] = now (ms) ARGV[2] = window_ms
-- ARGV[3] = bucket_max ARGV[4] = daily_max
-- ARGV[5] = request_id ARGV[6] = daily_ttl
-- Trim sliding window
redis
.
call
(
'ZREMRANGEBYSCORE'
,
KEYS
[
1
]
,
0
,
ARGV
[
1
]
-
ARGV
[
2
]
)
local
burst_used
=
redis
.
call
(
'ZCARD'
,
KEYS
[
1
]
)
local
daily_used
=
tonumber
(
redis
.
call
(
'GET'
,
KEYS
[
2
]
)
or
'0'
)
if
burst_used
>=
tonumber
(
ARGV
[
3
]
)
then
-- Tell caller how long to sleep until oldest entry expires
local
oldest
=
redis
.
call
(
'ZRANGE'
,
KEYS
[
1
]
,
0
,
0
,
'WITHSCORES'
)
return
{
0
,
'BURST'
,
(
oldest
[
2
]
+
ARGV
[
2
]
)
-
ARGV
[
1
]
}
end
if
daily_used
>=
tonumber
(
ARGV
[
4
]
)
then
return
{
0
,
'DAILY'
,
-
1
}
end
redis
.
call
(
'ZADD'
,
KEYS
[
1
]
,
ARGV
[
1
]
,
ARGV
[...
|
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Think carefully about the implementation and potential issue and bottlenecks.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Show more","depth":23,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"10:42","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Edit","depth":23,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Copy","depth":23,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"Claude responded: Current HubSpot limits (Apr 2026)","depth":22,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Claude responded: Current HubSpot limits (Apr 2026)","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms","depth":23,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Current HubSpot limits (Apr 2026)","depth":25,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Current HubSpot limits (Apr 2026)","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". The burst window is per app; the daily quota is per portal.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":27,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":27,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". That's the limit the 429 in your example is hitting (","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"secondly limit","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"on","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/crm/v3/objects/contact/search","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"). It's a separate bucket from the 190/10s, and critically, search responses do not include the","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-*","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"headers","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— you can't read remaining capacity off the response, you have to track it yourself.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":27,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":27,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Non-search endpoints return","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Max","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", and","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Interval-Milliseconds","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". The daily headers are not returned for OAuth-authenticated calls","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— that's enforced for marketplace listing, but it's also a sane operational target.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":27,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":27,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"So you have","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"four buckets","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Where to store it","depth":25,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Where to store it","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"INCR","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"EXPIRE","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"plus Lua scripting for true CAS semantics.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Key layout I'd use:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":28,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:search:{portalId} ZSET (sliding 1s)","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:meta:{portalId} HASH (last seen headers)","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The two short-window buckets are sliding-window-log (Redis ZSET, score =","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"microtime","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The daily bucket is a plain","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"INCR","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with TTL set to seconds-until-midnight in","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"HubSpot's account timezone","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to read it","depth":25,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to read it","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Two-phase: optimistic pre-check, authoritative post-update.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Before a call, run the Lua acquire script. If it returns \"denied,\" sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"After the call, parse","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Max","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Interval-Milliseconds","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and store them in","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:meta:{portalId}","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"For search specifically, since headers don't come back, the local counter","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"is","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"not","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"on 429 (you really did make that request).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to work with multiple jobs","depth":25,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to work with multiple jobs","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Concurrency cap","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"via","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Queue::throttle()","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Per-tenant fairness","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Priority lanes","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot-priority","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot-bulk","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Backoff on 429","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— respect the","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry-After","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"header. If absent, exponential with jitter (e.g.,","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"min(2^attempt * 250ms, 30s) ± 20%","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to work with paginated requests","depth":25,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to work with paginated requests","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The trap is treating \"fetch all\" as one logical operation. Each page is its own API call and competes for tokens with everything else.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Two practical rules:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Don't hold the worker idle while paginating.","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"processing","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"of page N as a separate job, and queue a \"fetch page N+1\" job with a delay equal to the wait time. Each page becomes its own atomic unit.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Always batch where HubSpot offers it.","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/{type}/batch/{read|update|create}","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"accepts up to 100 IDs per call. For your \"patching IDs\" flow this is the difference between 600 calls and 6.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Walkthrough: 600 opportunities","depth":25,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Walkthrough: 600 opportunities","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Sane flow:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Identify deltas","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— if you can avoid Search at all, do. Use","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Batch read","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"current state —","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/deals/batch/read","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with 100 IDs per call → 6 calls. Burst weight 6.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Compute diff","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"in your code (no API).","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Batch update","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"—","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/deals/batch/update","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with 100 per call → 6 calls.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Associations","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"—","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v4/associations/{from}/{to}/batch/create","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"if needed → another few batch calls.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Recognition: pre-check denies if","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"<weight>","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"slots aren't free; on 429 the response surfaces","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"policyName","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(DAILY/SECONDLY) — log it, push the job back with","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry-After","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"delay, increment a metric so you can alarm on sustained 429 rate.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Pitfalls & bottlenecks worth pre-empting","depth":25,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Pitfalls & bottlenecks worth pre-empting","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Daily quota is shared across the whole portal","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search is uniquely fragile","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":": separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hs_object_id","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"ranges or by","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"windows.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry storms after HubSpot incidents","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Webhook → sync → webhook loops","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"jy_last_sync_id","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":") and ignore webhooks where it just changed.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Token refresh","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"expires_in - 60s","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", single-flight the refresh (only one worker refreshes; others wait on a lock).","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search latency for fresh writes","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Clock skew","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— Redis is authoritative. Don't use","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"time()","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"from each worker for window scoring; use","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"redis.call('TIME')","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"inside the Lua script.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Dead workers with held tokens","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The 5% error rule","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Phantom contention with Laravel","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"RateLimited","depth":29,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"middleware","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":": it's per-job-class by default, not per-portal. 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Can I still continue making search requests once I hit the normal requests limits?","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"13:40","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Edit","depth":23,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Copy","depth":23,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"Claude responded: They're independent buckets — with one shared bucket on top.","depth":22,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Claude responded: They're independent buckets — with one shared bucket on top.","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Analyzed independent rate limit buckets and their practical interactions","depth":23,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Analyzed independent rate limit buckets and their practical interactions","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"They're independent buckets — with one shared bucket on top.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"The three buckets and what each call hits","depth":25,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"The three buckets and what each call hits","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":28,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"┌──────────────────┐","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search request ──→ │ Search: 5/sec │ ──┐","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"└──────────────────┘ │","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"├──→ ┌─────────────────┐","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"┌──────────────────┐ │ │ Daily: 250k–1M │","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"└──────────────────┘ └─────────────────┘","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"A search call is only metered against the search bucket and the daily bucket. 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Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"13:44","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Edit","depth":23,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Copy","depth":23,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.","depth":22,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Analyzed per-request overhead concerns and quantified Redis performance impact","depth":23,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Analyzed per-request overhead concerns and quantified Redis performance impact","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out. 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HubSpot rate limit implementation strategy, rename chat
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Claude is responding
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
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Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
Copy
Give positive feedback
Give negative feedback
You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
Edit
Copy
Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
Copy to clipboard
php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.
Copy
Give positive feedback
Give negative feedback
You said: Ok, so what will happen each time I make a request?
You said: Ok, so what will happen each time I make a request?
Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?
13:44
Edit
Copy
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Analyzed per-request overhead concerns and quantified Redis performance impact
Analyzed per-request overhead concerns and quantified Redis performance impact
Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out. But the overhead is much smaller than it sounds, and importantly, it's nowhere near your bottleneck.
What "two buckets per request" actually costs
What "two buckets per request" actually costs
Each request makes
one round trip to Redis
— not two — because you fold both bucket checks into a single Lua script that runs atomically server-side:
Copy to clipboard
lua
-- KEYS[1] = burst bucket key (or search)
-- KEYS[2] = daily bucket key
-- ARGV[1] = now (ms) ARGV[2] = window_ms
-- ARGV[3] = bucket_max ARGV[4] = daily_max
-- ARGV[5] = request_id ARGV[6] = daily_ttl
-- Trim sliding window
redis
.
call
(
'ZREMRANGEBYSCORE'
,
KEYS
[
1
]
,
0
,
ARGV
[
1
]
-
ARGV
[
2
]
)
local
burst_used
=
redis
.
call
(
'ZCARD'
,
KEYS
[
1
]
)
local
daily_used
=
tonumber
(
redis
.
call
(
'GET'
,
KEYS
[
2
]
)
or
'0'
)
if
burst_used
>=
tonumber
(
ARGV
[
3
]
)
then
-- Tell caller how long to sleep until oldest entry expires
local
oldest
=
redis
.
call
(
'ZRANGE'
,
KEYS
[
1
]
,
0
,
0
,
'WITHSCORES'
)
return
{
0
,
'BURST'
,
(
oldest
[
2
]
+
ARGV
[
2
]
)
-
ARGV
[
1
]
}
end
if
daily_used
>=
tonumber
(
ARGV
[
4
]
)
then
return
{
0
,
'DAILY'
,
-
1
}
end
redis
.
call
(
'ZADD'
,
KEYS
[
1
]
,
ARGV
[
1
]
,
ARGV
[...
|
2612
|
NULL
|
NULL
|
NULL
|
|
2615
|
110
|
21
|
2026-05-07T11:32:07.390189+00:00
|
/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778153527390_m2.jpg...
|
Claude
|
Claude
|
True
|
NULL
|
monitor_2
|
NULL
|
NULL
|
NULL
|
NULL
|
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Close
Claude is responding
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
Show more
10:42
Edit
Copy
Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
Copy
Give positive feedback
Give negative feedback
You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
Edit
Copy
Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
Copy to clipboard
php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.
Copy
Give positive feedback
Give negative feedback
You said: Ok, so what will happen each time I make a request?
You said: Ok, so what will happen each time I make a request?
Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?
13:44
Edit
Copy
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Analyzed per-request overhead concerns and quantified Redis performance impact
Analyzed per-request overhead concerns and quantified Redis performance impact
Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out. But the overhead is much smaller than it sounds, and importantly, it's nowhere near your bottleneck.
What "two buckets per request" actually costs
What "two buckets per request" actually costs
Each request makes
one round trip to Redis
— not two — because you fold both bucket checks into a single Lua script that runs atomically server-side:
Copy to clipboard
lua
-- KEYS[1] = burst bucket key (or search)
-- KEYS[2] = daily bucket key
-- ARGV[1] = now (ms) ARGV[2] = window_ms
-- ARGV[3] = bucket_max ARGV[4] = daily_max
-- ARGV[5] = request_id ARGV[6] = daily_ttl
-- Trim sliding window
redis
.
call
(
'ZREMRANGEBYSCORE'
,
KEYS
[
1
]
,
0
,
ARGV
[
1
]
-
ARGV
[
2
]
)
local
burst_used
=
redis
.
call
(
'ZCARD'
,
KEYS
[
1
]
)
local
daily_used
=
tonumber
(
redis
.
call
(
'GET'
,
KEYS
[
2
]
)
or
'0'
)
if
burst_used
>=
tonumber
(
ARGV
[
3
]
)
then
-- Tell caller how long to sleep until oldest entry expires
local
oldest
=
redis
.
call
(
'ZRANGE'
,
KEYS
[
1
]
,
0
,
0
,
'WITHSCORES'
)
return
{
0
,
'BURST'
,
(
oldest
[
2
]
+
ARGV
[
2
]
)
-
ARGV
[
1
]
}
end
if
daily_used
>=
tonumber
(
ARGV
[
4
]
)
then
return...
|
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The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"updating activity (engagement, meeting, call, note)","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"searching for a company or contact","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"updating deal","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"patching IDs\nReview what the limit is at the moment in the HubSpot documentation and then think of the solution:","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"where to store it","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"how to read it","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"how to work with multiple jobs","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"). Example of such a error Client error:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST https://api.hubapi.com/crm/v3/objects/contact/search","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"resulted in a","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"429 Too Many Requests","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"response: {\"status\":\"error\",\"message\":\"You have reached your secondly limit.\",\"errorType\":\"RATE_LIMIT\",\"correlationId\":\"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Show more","depth":23,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"10:42","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Edit","depth":23,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Copy","depth":23,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"Claude responded: Current HubSpot limits (Apr 2026)","depth":22,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Claude responded: Current HubSpot limits (Apr 2026)","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms","depth":23,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Current HubSpot limits (Apr 2026)","depth":25,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Current HubSpot limits (Apr 2026)","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". The burst window is per app; the daily quota is per portal.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":27,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":27,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". That's the limit the 429 in your example is hitting (","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"secondly limit","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"on","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/crm/v3/objects/contact/search","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"). It's a separate bucket from the 190/10s, and critically, search responses do not include the","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-*","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"headers","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— you can't read remaining capacity off the response, you have to track it yourself.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":27,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":27,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Non-search endpoints return","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Max","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", and","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Interval-Milliseconds","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". The daily headers are not returned for OAuth-authenticated calls","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— that's enforced for marketplace listing, but it's also a sane operational target.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":27,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":27,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"So you have","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"four buckets","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Where to store it","depth":25,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Where to store it","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"INCR","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"EXPIRE","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"plus Lua scripting for true CAS semantics.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Key layout I'd use:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":28,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:search:{portalId} ZSET (sliding 1s)","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:meta:{portalId} HASH (last seen headers)","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The two short-window buckets are sliding-window-log (Redis ZSET, score =","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"microtime","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The daily bucket is a plain","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"INCR","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with TTL set to seconds-until-midnight in","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"HubSpot's account timezone","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to read it","depth":25,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to read it","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Two-phase: optimistic pre-check, authoritative post-update.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Before a call, run the Lua acquire script. If it returns \"denied,\" sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"After the call, parse","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Max","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Interval-Milliseconds","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and store them in","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:meta:{portalId}","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"For search specifically, since headers don't come back, the local counter","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"is","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"not","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"on 429 (you really did make that request).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to work with multiple jobs","depth":25,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to work with multiple jobs","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Concurrency cap","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"via","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Queue::throttle()","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Per-tenant fairness","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Priority lanes","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot-priority","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot-bulk","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Backoff on 429","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— respect the","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry-After","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"header. If absent, exponential with jitter (e.g.,","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"min(2^attempt * 250ms, 30s) ± 20%","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to work with paginated requests","depth":25,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to work with paginated requests","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The trap is treating \"fetch all\" as one logical operation. Each page is its own API call and competes for tokens with everything else.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Two practical rules:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Don't hold the worker idle while paginating.","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"processing","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"of page N as a separate job, and queue a \"fetch page N+1\" job with a delay equal to the wait time. Each page becomes its own atomic unit.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Always batch where HubSpot offers it.","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/{type}/batch/{read|update|create}","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"accepts up to 100 IDs per call. For your \"patching IDs\" flow this is the difference between 600 calls and 6.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Walkthrough: 600 opportunities","depth":25,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Walkthrough: 600 opportunities","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Sane flow:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Identify deltas","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— if you can avoid Search at all, do. Use","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Batch read","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"current state —","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/deals/batch/read","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with 100 IDs per call → 6 calls. Burst weight 6.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Compute diff","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"in your code (no API).","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Batch update","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"—","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/deals/batch/update","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with 100 per call → 6 calls.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Associations","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"—","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v4/associations/{from}/{to}/batch/create","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"if needed → another few batch calls.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Recognition: pre-check denies if","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"<weight>","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"slots aren't free; on 429 the response surfaces","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"policyName","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(DAILY/SECONDLY) — log it, push the job back with","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry-After","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"delay, increment a metric so you can alarm on sustained 429 rate.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Pitfalls & bottlenecks worth pre-empting","depth":25,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Pitfalls & bottlenecks worth pre-empting","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Daily quota is shared across the whole portal","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search is uniquely fragile","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":": separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hs_object_id","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"ranges or by","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"windows.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry storms after HubSpot incidents","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Webhook → sync → webhook loops","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"jy_last_sync_id","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":") and ignore webhooks where it just changed.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Token refresh","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"expires_in - 60s","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", single-flight the refresh (only one worker refreshes; others wait on a lock).","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search latency for fresh writes","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Clock skew","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— Redis is authoritative. Don't use","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"time()","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"from each worker for window scoring; use","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"redis.call('TIME')","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"inside the Lua script.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Dead workers with held tokens","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The 5% error rule","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Phantom contention with Laravel","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"RateLimited","depth":29,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"middleware","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":": it's per-job-class by default, not per-portal. If your","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"SyncDealJob","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"UpdateActivityJob","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"HubspotRateLimiter","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"service that everything routes through.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy","depth":23,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give positive feedback","depth":23,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give negative feedback","depth":23,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"You said: What will happen if there are two different types of rate limits?","depth":21,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"You said: What will happen if there are two different types of rate limits?","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"What will happen if there are two different types of rate limits?","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search requests that have 5 requests per second","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"13:40","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Edit","depth":23,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Copy","depth":23,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"Claude responded: They're independent buckets — with one shared bucket on top.","depth":22,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Claude responded: They're independent buckets — with one shared bucket on top.","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Analyzed independent rate limit buckets and their practical interactions","depth":23,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Analyzed independent rate limit buckets and their practical interactions","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"They're independent buckets — with one shared bucket on top.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"The three buckets and what each call hits","depth":25,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"The three buckets and what each call hits","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":28,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"┌──────────────────┐","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search request ──→ │ Search: 5/sec │ ──┐","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"└──────────────────┘ │","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"├──→ ┌─────────────────┐","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"┌──────────────────┐ │ │ Daily: 250k–1M │","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"└──────────────────┘ └─────────────────┘","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"What this means practically","depth":25,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"What this means practically","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting search secondly limit (5/s):","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Only further","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/search","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting burst limit (190/10s):","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting daily limit:","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"So can one type of request block the other?","depth":25,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"So can one type of request block the other?","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search → Burst:","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"No.","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Burst → Search:","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"No.","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Saturating burst leaves Search's 5/s untouched.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Either → Daily:","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Yes","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", but only by contributing to the same daily counter. It's volume that kills you here, not the type.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"In theory you can sustain ~50 search + ~190 other =","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"~240 calls per 10 seconds","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"indefinitely (until daily runs out), because the two buckets bill independently.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Implementation consequence","depth":25,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Implementation consequence","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":28,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"php","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'search'","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// for search endpoints only","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'burst'","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// for everything else","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'daily'","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// every call, both kinds","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('search')","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"+","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('daily')","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". A batch update calls","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('burst')","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"+","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('daily')","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":".","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"One subtle pitfall: when you backoff on a 429,","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"inspect","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"policyName","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"in the response body","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to know which bucket actually fired. If you see","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"SECONDLY","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The other subtlety: the burst response gives you","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy","depth":23,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give positive feedback","depth":23,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give negative feedback","depth":23,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"You said: Ok, so what will happen each time I make a request?","depth":21,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"You said: Ok, so what will happen each time I make a request?","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"13:44","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Edit","depth":23,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Copy","depth":23,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.","depth":22,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Analyzed per-request overhead concerns and quantified Redis performance impact","depth":23,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Analyzed per-request overhead concerns and quantified Redis performance impact","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out. But the overhead is much smaller than it sounds, and importantly, it's nowhere near your bottleneck.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"What \"two buckets per request\" actually costs","depth":25,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"What \"two buckets per request\" actually costs","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Each request makes","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"one round trip to Redis","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— not two — because you fold both bucket checks into a single Lua script that runs atomically server-side:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":28,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"lua","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-- KEYS[1] = burst bucket key (or search)","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-- KEYS[2] = daily bucket key","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-- ARGV[1] = now (ms) ARGV[2] = window_ms","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-- ARGV[3] = bucket_max ARGV[4] = daily_max","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-- ARGV[5] = request_id ARGV[6] = daily_ttl","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-- Trim sliding window","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"redis","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":".","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"call","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'ZREMRANGEBYSCORE'","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"KEYS","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"[","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"]","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"0","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"ARGV","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"[","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"]","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"ARGV","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"[","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"2","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"]","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"local","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"burst_used","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"redis","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":".","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"call","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'ZCARD'","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"KEYS","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"[","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"]","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"local","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"daily_used","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"tonumber","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"redis","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":".","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"call","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'GET'","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":28,"on_screen":false,"role_descriptio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Tell caller how long to sleep until oldest entry expires","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"local","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"oldest","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"redis","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":".","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"call","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'ZRANGE'","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"KEYS","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"[","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"]","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"0","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"0","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'WITHSCORES'","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"return","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"{","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"0","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'BURST'","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"oldest","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"[","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"2","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"]","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"+","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"ARGV","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"[","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"2","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"]","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"ARGV","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"[","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"]","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text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Claude is responding
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
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Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
Copy
Give positive feedback
Give negative feedback
You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
Edit
Copy
Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
Copy to clipboard
php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.
Copy
Give positive feedback
Give negative feedback
You said: Ok, so what will happen each time I make a request?
You said: Ok, so what will happen each time I make a request?
Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?
13:44
Edit
Copy
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Analyzed per-request overhead concerns and quantified Redis performance impact
Analyzed per-request overhead concerns and quantified Redis performance impact
Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out. But the overhead is much smaller than it sounds, and importantly, it's nowhere near your bottleneck.
What "two buckets per request" actually costs
What "two buckets per request" actually costs
Each request makes
one round trip to Redis
— not two — because you fold both bucket checks into a single Lua script that runs atomically server-side:
Copy to clipboard
lua
-- KEYS[1] = burst bucket key (or search)
-- KEYS[2] = daily bucket key
-- ARGV[1] = now (ms) ARGV[2] = window_ms
-- ARGV[3] = bucket_max ARGV[4] = daily_max
-- ARGV[5] = request_id ARGV[6] = daily_ttl
-- Trim sliding window
redis
.
call
(
'ZREMRANGEBYSCORE'
,
KEYS
[
1
]
,
0
,
ARGV
[
1
]
-
ARGV
[
2
]
)
local
burst_used
=
redis
.
call
(
'ZCARD'
,
KEYS
[
1
]
)
local
daily_used
=
tonumber
(
redis
.
call
(
'GET'
,
KEYS
[
2
]
)
or
'0'
)
if
burst_used
>=
tonumber
(
ARGV
[
3
]
)
then
-- Tell caller how long to sleep until oldest entry expires
local
oldest
=
redis
.
call
(
'ZRANGE'
,
KEYS
[
1
]
,
0
,
0
,
'WITHSCORES'
)
return
{
0
,
'BURST'
,
(
oldest
[
2
]
+
ARGV
[
2
]
)
-
ARGV
[
1
]
}
end
if
daily_used
>=
tonumber
(
ARGV
[
4
]
)
then
return...
|
NULL
|
NULL
|
NULL
|
NULL
|
|
2617
|
110
|
22
|
2026-05-07T11:32:12.324237+00:00
|
/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778153532324_m2.jpg...
|
Claude
|
Claude
|
True
|
NULL
|
monitor_2
|
NULL
|
NULL
|
NULL
|
NULL
|
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Close
Claude is responding
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
Show more
10:42
Edit
Copy
Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is...
|
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Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The daily bucket is a plain","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"INCR","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with TTL set to seconds-until-midnight in","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"HubSpot's account timezone","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to read it","depth":25,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to read it","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Two-phase: optimistic pre-check, authoritative post-update.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Before a call, run the Lua acquire script. If it returns \"denied,\" sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"After the call, parse","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Max","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Interval-Milliseconds","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and store them in","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:meta:{portalId}","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"For search specifically, since headers don't come back, the local counter","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"is","depth":27,"on_screen":false,"role_description":"text"}]...
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Claude is responding
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
Show more
10:42
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Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is...
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/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778153536482_m2.jpg...
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Close
Claude is responding
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
Show more
10:42
Edit
Copy
Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
Copy
Give positive feedback
Give negative feedback
You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
Edit
Copy
Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further...
|
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The burst window is per app; the daily quota is per portal.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":27,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":27,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". That's the limit the 429 in your example is hitting (","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"secondly limit","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"on","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/crm/v3/objects/contact/search","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"). It's a separate bucket from the 190/10s, and critically, search responses do not include the","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-*","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"headers","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— you can't read remaining capacity off the response, you have to track it yourself.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":27,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":27,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Non-search endpoints return","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Max","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", and","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Interval-Milliseconds","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". The daily headers are not returned for OAuth-authenticated calls","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— that's enforced for marketplace listing, but it's also a sane operational target.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":27,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":27,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"So you have","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"four buckets","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Where to store it","depth":25,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Where to store it","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"INCR","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"EXPIRE","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"plus Lua scripting for true CAS semantics.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Key layout I'd use:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":28,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:search:{portalId} ZSET (sliding 1s)","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:meta:{portalId} HASH (last seen headers)","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The two short-window buckets are sliding-window-log (Redis ZSET, score =","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"microtime","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The daily bucket is a plain","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"INCR","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with TTL set to seconds-until-midnight in","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"HubSpot's account timezone","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to read it","depth":25,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to read it","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Two-phase: optimistic pre-check, authoritative post-update.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Before a call, run the Lua acquire script. If it returns \"denied,\" sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"After the call, parse","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Max","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Interval-Milliseconds","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and store them in","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:meta:{portalId}","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"For search specifically, since headers don't come back, the local counter","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"is","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"not","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"on 429 (you really did make that request).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to work with multiple jobs","depth":25,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to work with multiple jobs","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Concurrency cap","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"via","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Queue::throttle()","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Per-tenant fairness","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Priority lanes","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot-priority","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot-bulk","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Backoff on 429","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— respect the","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry-After","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"header. If absent, exponential with jitter (e.g.,","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"min(2^attempt * 250ms, 30s) ± 20%","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to work with paginated requests","depth":25,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to work with paginated requests","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The trap is treating \"fetch all\" as one logical operation. Each page is its own API call and competes for tokens with everything else.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Two practical rules:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Don't hold the worker idle while paginating.","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"processing","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"of page N as a separate job, and queue a \"fetch page N+1\" job with a delay equal to the wait time. Each page becomes its own atomic unit.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Always batch where HubSpot offers it.","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/{type}/batch/{read|update|create}","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"accepts up to 100 IDs per call. For your \"patching IDs\" flow this is the difference between 600 calls and 6.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Walkthrough: 600 opportunities","depth":25,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Walkthrough: 600 opportunities","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Sane flow:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Identify deltas","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— if you can avoid Search at all, do. Use","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Batch read","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"current state —","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/deals/batch/read","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with 100 IDs per call → 6 calls. Burst weight 6.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Compute diff","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"in your code (no API).","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Batch update","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"—","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/deals/batch/update","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with 100 per call → 6 calls.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Associations","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"—","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v4/associations/{from}/{to}/batch/create","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"if needed → another few batch calls.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Recognition: pre-check denies if","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"<weight>","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"slots aren't free; on 429 the response surfaces","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"policyName","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(DAILY/SECONDLY) — log it, push the job back with","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry-After","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"delay, increment a metric so you can alarm on sustained 429 rate.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Pitfalls & bottlenecks worth pre-empting","depth":25,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Pitfalls & bottlenecks worth pre-empting","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Daily quota is shared across the whole portal","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search is uniquely fragile","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":": separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hs_object_id","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"ranges or by","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"windows.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry storms after HubSpot incidents","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Webhook → sync → webhook loops","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"jy_last_sync_id","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":") and ignore webhooks where it just changed.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Token refresh","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"expires_in - 60s","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", single-flight the refresh (only one worker refreshes; others wait on a lock).","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search latency for fresh writes","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Clock skew","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— Redis is authoritative. Don't use","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"time()","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"from each worker for window scoring; use","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"redis.call('TIME')","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"inside the Lua script.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Dead workers with held tokens","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The 5% error rule","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Phantom contention with Laravel","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"RateLimited","depth":29,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"middleware","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":": it's per-job-class by default, not per-portal. If your","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"SyncDealJob","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"UpdateActivityJob","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"HubspotRateLimiter","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"service that everything routes through.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy","depth":23,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give positive feedback","depth":23,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give negative feedback","depth":23,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"You said: What will happen if there are two different types of rate limits?","depth":21,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"You said: What will happen if there are two different types of rate limits?","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"What will happen if there are two different types of rate limits?","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search requests that have 5 requests per second","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"13:40","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Edit","depth":23,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Copy","depth":23,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"Claude responded: They're independent buckets — with one shared bucket on top.","depth":22,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Claude responded: They're independent buckets — with one shared bucket on top.","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Analyzed independent rate limit buckets and their practical interactions","depth":23,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Analyzed independent rate limit buckets and their practical interactions","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"They're independent buckets — with one shared bucket on top.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"The three buckets and what each call hits","depth":25,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"The three buckets and what each call hits","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":28,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"┌──────────────────┐","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search request ──→ │ Search: 5/sec │ ──┐","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"└──────────────────┘ │","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"├──→ ┌─────────────────┐","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"┌──────────────────┐ │ │ Daily: 250k–1M │","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"└──────────────────┘ └─────────────────┘","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"What this means practically","depth":25,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"What this means practically","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting search secondly limit (5/s):","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Only further","depth":26,"on_screen":false,"role_description":"text"}]...
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Claude is responding
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
Show more
10:42
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Copy
Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
Copy
Give positive feedback
Give negative feedback
You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
Edit
Copy
Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further...
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HubSpot rate limit implementation strategy, rename chat
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More options for HubSpot rate limit implementation strategy
Close
Claude is responding
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
Show more
10:42
Edit
Copy
Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
Copy
Give positive feedback
Give negative feedback
You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
Edit
Copy
Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
Copy to clipboard
php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.
Copy
Give positive feedback
Give negative feedback
You said: Ok, so what will happen each time I make a request?
You said: Ok, so what will happen each time I make a request?
Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?
13:44
Edit
Copy
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Analyzed per-request overhead concerns and quantified Redis performance impact
Analyzed per-request overhead concerns and quantified Redis performance impact
Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out. But the overhead is much smaller than it sounds, and importantly, it's nowhere near your bottleneck.
What "two buckets per request" actually costs
What "two buckets per request" actually costs
Each request makes
one round trip to Redis
— not two — because you fold both bucket checks into a single Lua script that runs atomically server-side:
Copy to clipboard
lua
-- KEYS[1] = burst bucket key (or search)
-- KEYS[2] = daily bucket key
-- ARGV[1] = now (ms) ARGV[2] = window_ms
-- ARGV[3] = bucket_max ARGV[4] = daily_max
-- ARGV[5] = request_id ARGV[6] = daily_ttl
-- Trim sliding window
redis
.
call
(
'ZREMRANGEBYSCORE'
,
KEYS
[
1
]
,
0
,
ARGV
[
1
]
-
ARGV
[
2
]
)
local
burst_used
=
redis
.
call
(
'ZCARD'
,
KEYS
[
1
]
)
local
daily_used
=
tonumber
(
redis
.
call
(
'GET'
,
KEYS
[
2
]
)
or
'0'
)
if
burst_used
>=
tonumber
(
ARGV
[
3
]
)
then
-- Tell caller how long to sleep until oldest entry expires
local
oldest
=
redis
.
call
(
'ZRANGE'
,
KEYS
[
1
]
,
0
,
0
,
'WITHSCORES'
)
return
{
0
,
'BURST'
,
(
oldest
[
2
]
+
ARGV
[
2
]
)
-
ARGV
[
1
]
}
end
if
daily_used
>=
tonumber
(
ARGV
[
4
]
)
then
return
{
0
,
'DAILY'
,
-
1
}
end
redis
.
call
(
'ZADD'
,
KEYS
[
1
]
,
ARGV
[
1
]
,
ARGV
[...
|
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Two queues:","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot-priority","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot-bulk","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Backoff on 429","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— respect the","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry-After","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"header. If absent, exponential with jitter (e.g.,","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"min(2^attempt * 250ms, 30s) ± 20%","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to work with paginated requests","depth":25,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to work with paginated requests","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The trap is treating \"fetch all\" as one logical operation. Each page is its own API call and competes for tokens with everything else.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Two practical rules:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Don't hold the worker idle while paginating.","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"processing","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"of page N as a separate job, and queue a \"fetch page N+1\" job with a delay equal to the wait time. Each page becomes its own atomic unit.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Always batch where HubSpot offers it.","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/{type}/batch/{read|update|create}","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"accepts up to 100 IDs per call. For your \"patching IDs\" flow this is the difference between 600 calls and 6.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Walkthrough: 600 opportunities","depth":25,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Walkthrough: 600 opportunities","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Sane flow:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Identify deltas","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— if you can avoid Search at all, do. Use","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Batch read","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"current state —","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/deals/batch/read","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with 100 IDs per call → 6 calls. Burst weight 6.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Compute diff","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"in your code (no API).","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Batch update","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"—","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/deals/batch/update","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with 100 per call → 6 calls.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Associations","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"—","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v4/associations/{from}/{to}/batch/create","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"if needed → another few batch calls.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Recognition: pre-check denies if","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"<weight>","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"slots aren't free; on 429 the response surfaces","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"policyName","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(DAILY/SECONDLY) — log it, push the job back with","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry-After","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"delay, increment a metric so you can alarm on sustained 429 rate.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Pitfalls & bottlenecks worth pre-empting","depth":25,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Pitfalls & bottlenecks worth pre-empting","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Daily quota is shared across the whole portal","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search is uniquely fragile","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":": separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hs_object_id","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"ranges or by","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"windows.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry storms after HubSpot incidents","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Webhook → sync → webhook loops","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"jy_last_sync_id","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":") and ignore webhooks where it just changed.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Token refresh","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"expires_in - 60s","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", single-flight the refresh (only one worker refreshes; others wait on a lock).","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search latency for fresh writes","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Clock skew","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— Redis is authoritative. Don't use","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"time()","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"from each worker for window scoring; use","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"redis.call('TIME')","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"inside the Lua script.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Dead workers with held tokens","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The 5% error rule","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Phantom contention with Laravel","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"RateLimited","depth":29,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"middleware","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":": it's per-job-class by default, not per-portal. If your","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"SyncDealJob","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"UpdateActivityJob","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"HubspotRateLimiter","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"service that everything routes through.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy","depth":23,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give positive feedback","depth":23,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give negative feedback","depth":23,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"You said: What will happen if there are two different types of rate limits?","depth":21,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"You said: What will happen if there are two different types of rate limits?","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"What will happen if there are two different types of rate limits?","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search requests that have 5 requests per second","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"13:40","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Edit","depth":23,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Copy","depth":23,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"Claude responded: They're independent buckets — with one shared bucket on top.","depth":22,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Claude responded: They're independent buckets — with one shared bucket on top.","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Analyzed independent rate limit buckets and their practical interactions","depth":23,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Analyzed independent rate limit buckets and their practical interactions","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"They're independent buckets — with one shared bucket on top.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"The three buckets and what each call hits","depth":25,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"The three buckets and what each call hits","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":28,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"┌──────────────────┐","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search request ──→ │ Search: 5/sec │ ──┐","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"└──────────────────┘ │","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"├──→ ┌─────────────────┐","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"┌──────────────────┐ │ │ Daily: 250k–1M │","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"└──────────────────┘ └─────────────────┘","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"What this means practically","depth":25,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"What this means practically","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting search secondly limit (5/s):","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Only further","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/search","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting burst limit (190/10s):","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting daily limit:","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"So can one type of request block the other?","depth":25,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"So can one type of request block the other?","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search → Burst:","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"No.","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Burst → Search:","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"No.","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Saturating burst leaves Search's 5/s untouched.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Either → Daily:","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Yes","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", but only by contributing to the same daily counter. It's volume that kills you here, not the type.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"In theory you can sustain ~50 search + ~190 other =","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"~240 calls per 10 seconds","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"indefinitely (until daily runs out), because the two buckets bill independently.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Implementation consequence","depth":25,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Implementation consequence","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":28,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"php","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'search'","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// for search endpoints only","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'burst'","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// for everything else","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'daily'","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// every call, both kinds","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('search')","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"+","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('daily')","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". A batch update calls","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('burst')","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"+","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('daily')","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":".","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"One subtle pitfall: when you backoff on a 429,","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"inspect","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"policyName","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"in the response body","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to know which bucket actually fired. 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So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy","depth":23,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give positive feedback","depth":23,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give negative feedback","depth":23,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"You said: Ok, so what will happen each time I make a request?","depth":21,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"You said: Ok, so what will happen each time I make a request?","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Ok, so what will happen each time I make a request? 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Close
Claude is responding
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
Show more
10:42
Edit
Copy
Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
Copy
Give positive feedback
Give negative feedback
You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
Edit
Copy
Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
Copy to clipboard
php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.
Copy
Give positive feedback
Give negative feedback
You said: Ok, so what will happen each time I make a request?
You said: Ok, so what will happen each time I make a request?
Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?
13:44
Edit
Copy
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Analyzed per-request overhead concerns and quantified Redis performance impact
Analyzed per-request overhead concerns and quantified Redis performance impact
Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out. But the overhead is much smaller than it sounds, and importantly, it's nowhere near your bottleneck.
What "two buckets per request" actually costs
What "two buckets per request" actually costs
Each request makes
one round trip to Redis
— not two — because you fold both bucket checks into a single Lua script that runs atomically server-side:
Copy to clipboard
lua
-- KEYS[1] = burst bucket key (or search)
-- KEYS[2] = daily bucket key
-- ARGV[1] = now (ms) ARGV[2] = window_ms
-- ARGV[3] = bucket_max ARGV[4] = daily_max
-- ARGV[5] = request_id ARGV[6] = daily_ttl
-- Trim sliding window
redis
.
call
(
'ZREMRANGEBYSCORE'
,
KEYS
[
1
]
,
0
,
ARGV
[
1
]
-
ARGV
[
2
]
)
local
burst_used
=
redis
.
call
(
'ZCARD'
,
KEYS
[
1
]
)
local
daily_used
=
tonumber
(
redis
.
call
(
'GET'
,
KEYS
[
2
]
)
or
'0'
)
if
burst_used
>=
tonumber
(
ARGV
[
3
]
)
then
-- Tell caller how long to sleep until oldest entry expires
local
oldest
=
redis
.
call
(
'ZRANGE'
,
KEYS
[
1
]
,
0
,
0
,
'WITHSCORES'
)
return
{
0
,
'BURST'
,
(
oldest
[
2
]
+
ARGV
[
2
]
)
-
ARGV
[
1
]
}
end
if
daily_used
>=
tonumber
(
ARGV
[
4
]
)
then
return
{
0
,
'DAILY'
,
-
1
}
end
redis
.
call
(
'ZADD'
,
KEYS
[
1
]
,
ARGV
[
1
]
,
ARGV
[...
|
2618
|
NULL
|
NULL
|
NULL
|
|
2621
|
110
|
25
|
2026-05-07T11:32:29.808819+00:00
|
/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778153549808_m2.jpg...
|
Claude
|
Claude
|
True
|
NULL
|
monitor_2
|
NULL
|
NULL
|
NULL
|
NULL
|
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Close
Claude is responding
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
Show more
10:42
Edit
Copy
Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
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Give positive feedback
Give negative feedback
You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
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Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
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┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
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php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.
Copy
Give positive feedback
Give negative feedback
You said: Ok, so what will happen each time I make a request?
You said: Ok, so what will happen each time I make a request?
Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?
13:44
Edit
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|
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The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. 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What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"). Example of such a error Client error:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST https://api.hubapi.com/crm/v3/objects/contact/search","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"resulted in a","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"429 Too Many Requests","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"response: {\"status\":\"error\",\"message\":\"You have reached your secondly limit.\",\"errorType\":\"RATE_LIMIT\",\"correlationId\":\"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Show more","depth":23,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"10:42","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Edit","depth":23,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Copy","depth":23,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"Claude responded: Current HubSpot limits (Apr 2026)","depth":22,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Claude responded: Current HubSpot limits (Apr 2026)","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms","depth":23,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Current HubSpot limits (Apr 2026)","depth":25,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Current HubSpot limits (Apr 2026)","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". The burst window is per app; the daily quota is per portal.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":27,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":27,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". That's the limit the 429 in your example is hitting (","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"secondly limit","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"on","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/crm/v3/objects/contact/search","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"). It's a separate bucket from the 190/10s, and critically, search responses do not include the","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-*","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"headers","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— you can't read remaining capacity off the response, you have to track it yourself.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":27,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":27,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Non-search endpoints return","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Max","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", and","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Interval-Milliseconds","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". The daily headers are not returned for OAuth-authenticated calls","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— that's enforced for marketplace listing, but it's also a sane operational target.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":27,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":27,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"So you have","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"four buckets","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Where to store it","depth":25,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Where to store it","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Redis. 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This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"For search specifically, since headers don't come back, the local counter","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"is","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"not","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"on 429 (you really did make that request).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to work with multiple jobs","depth":25,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to work with multiple jobs","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Concurrency cap","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"via","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Queue::throttle()","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Per-tenant fairness","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Priority lanes","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot-priority","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot-bulk","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Backoff on 429","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— respect the","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry-After","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"header. 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Each page is its own API call and competes for tokens with everything else.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Two practical rules:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Don't hold the worker idle while paginating.","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"processing","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"of page N as a separate job, and queue a \"fetch page N+1\" job with a delay equal to the wait time. Each page becomes its own atomic unit.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Always batch where HubSpot offers it.","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/{type}/batch/{read|update|create}","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"accepts up to 100 IDs per call. For your \"patching IDs\" flow this is the difference between 600 calls and 6.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Walkthrough: 600 opportunities","depth":25,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Walkthrough: 600 opportunities","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Sane flow:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Identify deltas","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— if you can avoid Search at all, do. Use","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Batch read","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"current state —","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/deals/batch/read","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with 100 IDs per call → 6 calls. Burst weight 6.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Compute diff","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"in your code (no API).","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Batch update","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"—","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/deals/batch/update","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with 100 per call → 6 calls.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Associations","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"—","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v4/associations/{from}/{to}/batch/create","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"if needed → another few batch calls.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Recognition: pre-check denies if","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"<weight>","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"slots aren't free; on 429 the response surfaces","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"policyName","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(DAILY/SECONDLY) — log it, push the job back with","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry-After","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"delay, increment a metric so you can alarm on sustained 429 rate.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Pitfalls & bottlenecks worth pre-empting","depth":25,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Pitfalls & bottlenecks worth pre-empting","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Daily quota is shared across the whole portal","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search is uniquely fragile","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":": separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hs_object_id","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"ranges or by","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"windows.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry storms after HubSpot incidents","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Webhook → sync → webhook loops","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"jy_last_sync_id","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":") and ignore webhooks where it just changed.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Token refresh","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"expires_in - 60s","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", single-flight the refresh (only one worker refreshes; others wait on a lock).","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search latency for fresh writes","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Clock skew","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— Redis is authoritative. Don't use","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"time()","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"from each worker for window scoring; use","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"redis.call('TIME')","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"inside the Lua script.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Dead workers with held tokens","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The 5% error rule","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Phantom contention with Laravel","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"RateLimited","depth":29,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"middleware","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":": it's per-job-class by default, not per-portal. If your","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"SyncDealJob","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"UpdateActivityJob","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"HubspotRateLimiter","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"service that everything routes through.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy","depth":23,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give positive feedback","depth":23,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give negative feedback","depth":23,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"You said: What will happen if there are two different types of rate limits?","depth":21,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"You said: What will happen if there are two different types of rate limits?","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"What will happen if there are two different types of rate limits?","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search requests that have 5 requests per second","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"13:40","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Edit","depth":23,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Copy","depth":23,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"Claude responded: They're independent buckets — with one shared bucket on top.","depth":22,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Claude responded: They're independent buckets — with one shared bucket on top.","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Analyzed independent rate limit buckets and their practical interactions","depth":23,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Analyzed independent rate limit buckets and their practical interactions","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"They're independent buckets — with one shared bucket on top.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"The three buckets and what each call hits","depth":25,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"The three buckets and what each call hits","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":28,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"┌──────────────────┐","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search request ──→ │ Search: 5/sec │ ──┐","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"└──────────────────┘ │","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"├──→ ┌─────────────────┐","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"┌──────────────────┐ │ │ Daily: 250k–1M │","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"└──────────────────┘ └─────────────────┘","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"What this means practically","depth":25,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"What this means practically","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting search secondly limit (5/s):","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Only further","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/search","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting burst limit (190/10s):","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting daily limit:","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"So can one type of request block the other?","depth":25,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"So can one type of request block the other?","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search → Burst:","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"No.","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Burst → Search:","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"No.","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Saturating burst leaves Search's 5/s untouched.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Either → Daily:","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Yes","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", but only by contributing to the same daily counter. It's volume that kills you here, not the type.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"In theory you can sustain ~50 search + ~190 other =","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"~240 calls per 10 seconds","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"indefinitely (until daily runs out), because the two buckets bill independently.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Implementation consequence","depth":25,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Implementation consequence","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":28,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"php","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'search'","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// for search endpoints only","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'burst'","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// for everything else","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'daily'","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// every call, both kinds","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('search')","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"+","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('daily')","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". A batch update calls","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('burst')","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"+","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('daily')","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":".","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"One subtle pitfall: when you backoff on a 429,","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"inspect","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"policyName","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"in the response body","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to know which bucket actually fired. If you see","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"SECONDLY","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The other subtlety: the burst response gives you","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy","depth":23,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give positive feedback","depth":23,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give negative feedback","depth":23,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"You said: Ok, so what will happen each time I make a request?","depth":21,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"You said: Ok, so what will happen each time I make a request?","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"13:44","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Edit","depth":23,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Copy","depth":23,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false}]...
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HubSpot rate limit implementation strategy, rename chat
HubSpot rate limit implementation strategy
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Claude is responding
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
Show more
10:42
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Copy
Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
Copy
Give positive feedback
Give negative feedback
You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
Edit
Copy
Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
Copy to clipboard
php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.
Copy
Give positive feedback
Give negative feedback
You said: Ok, so what will happen each time I make a request?
You said: Ok, so what will happen each time I make a request?
Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?
13:44
Edit
Copy...
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2026-05-07T11:32:37.633468+00:00
|
/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778153557633_m2.jpg...
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Claude
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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
Show more
10:42
Retry
Edit
Copy
Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
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You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
Retry
Edit
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Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
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┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
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php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.
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You said: Ok, so what will happen each time I make a request?
You said: Ok, so what will happen each time I make a request?
Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?
13:44
Retry
Edit
Copy
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Analyzed per-request overhead concerns and quantified Redis performance impact
Analyzed per-request overhead concerns and quantified Redis performance impact
Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out. But the overhead is much smaller than it sounds, and importantly, it's nowhere near your bottleneck.
What "two buckets per request" actually costs
What "two buckets per request" actually costs
Each request makes
one round trip to Redis
— not two — because you fold both bucket checks into a single Lua script that runs atomically server-side:
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lua
-- KEYS[1] = burst bucket key (or search)
-- KEYS[2] = daily bucket key
-- ARGV[1] = now (ms) ARGV[2] = window_ms
-- ARGV[3] = bucket_max ARGV[4] = daily_max
-- ARGV[5] = request_id ARGV[6] = daily_ttl
-- Trim sliding window
redis
.
call
(
'ZREMRANGEBYSCORE'
,
KEYS
[
1
]
,
0
,
ARGV
[
1
]
-
ARGV
[
2
]
)
local
burst_used
=
redis
.
call
(
'ZCARD'
,
KEYS
[
1
]
)
local
daily_used
=
tonumber
(
redis
.
call
(
'GET'
,
KEYS
[
2
]
)
or
'0'
)
if
burst_used
>=
tonumber
(
ARGV
[
3
]...
|
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The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"updating activity (engagement, meeting, call, note)","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"searching for a company or contact","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"updating deal","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"patching IDs\nReview what the limit is at the moment in the HubSpot documentation and then think of the solution:","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"where to store it","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"how to read it","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"how to work with multiple jobs","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"). Example of such a error Client error:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST https://api.hubapi.com/crm/v3/objects/contact/search","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"resulted in a","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"429 Too Many Requests","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"response: {\"status\":\"error\",\"message\":\"You have reached your secondly limit.\",\"errorType\":\"RATE_LIMIT\",\"correlationId\":\"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Show more","depth":23,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"10:42","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Retry","depth":23,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Edit","depth":23,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Copy","depth":23,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"Claude responded: Current HubSpot limits (Apr 2026)","depth":22,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Claude responded: Current HubSpot limits (Apr 2026)","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms","depth":23,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Current HubSpot limits (Apr 2026)","depth":25,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Current HubSpot limits (Apr 2026)","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". The burst window is per app; the daily quota is per portal.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":27,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":27,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". That's the limit the 429 in your example is hitting (","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"secondly limit","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"on","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/crm/v3/objects/contact/search","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"). It's a separate bucket from the 190/10s, and critically, search responses do not include the","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-*","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"headers","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— you can't read remaining capacity off the response, you have to track it yourself.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":27,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":27,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Non-search endpoints return","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Max","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", and","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Interval-Milliseconds","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". The daily headers are not returned for OAuth-authenticated calls","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— that's enforced for marketplace listing, but it's also a sane operational target.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":27,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":27,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"So you have","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"four buckets","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Where to store it","depth":25,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Where to store it","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Redis. 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Get this once and cache it.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to read it","depth":25,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to read it","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Two-phase: optimistic pre-check, authoritative post-update.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Before a call, run the Lua acquire script. If it returns \"denied,\" sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"After the call, parse","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Max","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Interval-Milliseconds","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and store them in","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:meta:{portalId}","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"For search specifically, since headers don't come back, the local counter","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"is","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"not","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"on 429 (you really did make that request).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to work with multiple jobs","depth":25,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to work with multiple jobs","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Concurrency cap","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"via","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Queue::throttle()","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Per-tenant fairness","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Priority lanes","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot-priority","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot-bulk","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Backoff on 429","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— respect the","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry-After","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"header. 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Each page is its own API call and competes for tokens with everything else.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Two practical rules:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Don't hold the worker idle while paginating.","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"processing","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"of page N as a separate job, and queue a \"fetch page N+1\" job with a delay equal to the wait time. Each page becomes its own atomic unit.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Always batch where HubSpot offers it.","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/{type}/batch/{read|update|create}","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"accepts up to 100 IDs per call. For your \"patching IDs\" flow this is the difference between 600 calls and 6.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Walkthrough: 600 opportunities","depth":25,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Walkthrough: 600 opportunities","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Sane flow:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Identify deltas","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— if you can avoid Search at all, do. Use","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Batch read","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"current state —","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/deals/batch/read","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with 100 IDs per call → 6 calls. Burst weight 6.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Compute diff","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"in your code (no API).","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Batch update","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"—","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/deals/batch/update","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with 100 per call → 6 calls.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Associations","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"—","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v4/associations/{from}/{to}/batch/create","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"if needed → another few batch calls.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Recognition: pre-check denies if","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"<weight>","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"slots aren't free; on 429 the response surfaces","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"policyName","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(DAILY/SECONDLY) — log it, push the job back with","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry-After","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"delay, increment a metric so you can alarm on sustained 429 rate.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Pitfalls & bottlenecks worth pre-empting","depth":25,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Pitfalls & bottlenecks worth pre-empting","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Daily quota is shared across the whole portal","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search is uniquely fragile","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":": separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hs_object_id","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"ranges or by","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"windows.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry storms after HubSpot incidents","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Webhook → sync → webhook loops","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"jy_last_sync_id","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":") and ignore webhooks where it just changed.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Token refresh","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"expires_in - 60s","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", single-flight the refresh (only one worker refreshes; others wait on a lock).","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search latency for fresh writes","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Clock skew","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— Redis is authoritative. Don't use","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"time()","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"from each worker for window scoring; use","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"redis.call('TIME')","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"inside the Lua script.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Dead workers with held tokens","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The 5% error rule","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Phantom contention with Laravel","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"RateLimited","depth":29,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"middleware","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":": it's per-job-class by default, not per-portal. If your","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"SyncDealJob","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"UpdateActivityJob","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"HubspotRateLimiter","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"service that everything routes through.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy","depth":23,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give positive feedback","depth":23,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give negative feedback","depth":23,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Retry","depth":23,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"You said: What will happen if there are two different types of rate limits?","depth":21,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"You said: What will happen if there are two different types of rate limits?","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"What will happen if there are two different types of rate limits?","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search requests that have 5 requests per second","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"13:40","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Retry","depth":23,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Edit","depth":23,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Copy","depth":23,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"Claude responded: They're independent buckets — with one shared bucket on top.","depth":22,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Claude responded: They're independent buckets — with one shared bucket on top.","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Analyzed independent rate limit buckets and their practical interactions","depth":23,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Analyzed independent rate limit buckets and their practical interactions","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"They're independent buckets — with one shared bucket on top.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"The three buckets and what each call hits","depth":25,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"The three buckets and what each call hits","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":28,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"┌──────────────────┐","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search request ──→ │ Search: 5/sec │ ──┐","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"└──────────────────┘ │","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"├──→ ┌─────────────────┐","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"┌──────────────────┐ │ │ Daily: 250k–1M │","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"└──────────────────┘ └─────────────────┘","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"What this means practically","depth":25,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"What this means practically","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting search secondly limit (5/s):","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Only further","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/search","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting burst limit (190/10s):","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting daily limit:","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"So can one type of request block the other?","depth":25,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"So can one type of request block the other?","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search → Burst:","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"No.","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Burst → Search:","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"No.","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Saturating burst leaves Search's 5/s untouched.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Either → Daily:","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Yes","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", but only by contributing to the same daily counter. It's volume that kills you here, not the type.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"In theory you can sustain ~50 search + ~190 other =","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"~240 calls per 10 seconds","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"indefinitely (until daily runs out), because the two buckets bill independently.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Implementation consequence","depth":25,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Implementation consequence","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":28,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"php","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'search'","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// for search endpoints only","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'burst'","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// for everything else","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'daily'","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// every call, both kinds","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('search')","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"+","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('daily')","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". A batch update calls","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('burst')","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"+","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('daily')","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":".","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"One subtle pitfall: when you backoff on a 429,","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"inspect","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"policyName","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"in the response body","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to know which bucket actually fired. If you see","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"SECONDLY","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The other subtlety: the burst response gives you","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy","depth":23,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give positive feedback","depth":23,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give negative feedback","depth":23,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Retry","depth":23,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"You said: Ok, so what will happen each time I make a request?","depth":21,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"You said: Ok, so what will happen each time I make a request?","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"13:44","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Retry","depth":23,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Edit","depth":23,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Copy","depth":23,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.","depth":22,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Analyzed per-request overhead concerns and quantified Redis performance impact","depth":23,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Analyzed per-request overhead concerns and quantified Redis performance impact","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out. But the overhead is much smaller than it sounds, and importantly, it's nowhere near your bottleneck.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"What \"two buckets per request\" actually costs","depth":25,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"What \"two buckets per request\" actually costs","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Each request makes","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"one round trip to Redis","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— not two — because you fold both bucket checks into a single Lua script that runs atomically server-side:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":28,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"lua","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-- KEYS[1] = burst bucket key (or search)","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-- KEYS[2] = daily bucket key","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-- ARGV[1] = now (ms) ARGV[2] = window_ms","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-- ARGV[3] = bucket_max ARGV[4] = daily_max","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-- ARGV[5] = request_id ARGV[6] = daily_ttl","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-- Trim sliding window","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"redis","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":".","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"call","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'ZREMRANGEBYSCORE'","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"KEYS","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"[","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"]","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"0","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"ARGV","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"[","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"]","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"ARGV","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"[","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"2","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"]","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"local","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"burst_used","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"redis","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":".","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"call","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'ZCARD'","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"KEYS","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"[","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"]","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"local","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"daily_used","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"tonumber","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"redis","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":".","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"call","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'GET'","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":28,"on_screen":false,"role_descriptio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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
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10:42
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Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
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You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
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Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
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┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
Copy to clipboard
php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.
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Retry
You said: Ok, so what will happen each time I make a request?
You said: Ok, so what will happen each time I make a request?
Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?
13:44
Retry
Edit
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Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Analyzed per-request overhead concerns and quantified Redis performance impact
Analyzed per-request overhead concerns and quantified Redis performance impact
Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out. But the overhead is much smaller than it sounds, and importantly, it's nowhere near your bottleneck.
What "two buckets per request" actually costs
What "two buckets per request" actually costs
Each request makes
one round trip to Redis
— not two — because you fold both bucket checks into a single Lua script that runs atomically server-side:
Copy to clipboard
lua
-- KEYS[1] = burst bucket key (or search)
-- KEYS[2] = daily bucket key
-- ARGV[1] = now (ms) ARGV[2] = window_ms
-- ARGV[3] = bucket_max ARGV[4] = daily_max
-- ARGV[5] = request_id ARGV[6] = daily_ttl
-- Trim sliding window
redis
.
call
(
'ZREMRANGEBYSCORE'
,
KEYS
[
1
]
,
0
,
ARGV
[
1
]
-
ARGV
[
2
]
)
local
burst_used
=
redis
.
call
(
'ZCARD'
,
KEYS
[
1
]
)
local
daily_used
=
tonumber
(
redis
.
call
(
'GET'
,
KEYS
[
2
]
)
or
'0'
)
if
burst_used
>=
tonumber
(
ARGV
[
3
]...
|
2621
|
NULL
|
NULL
|
NULL
|
|
2625
|
110
|
27
|
2026-05-07T11:32:40.876076+00:00
|
/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778153560876_m2.jpg...
|
Claude
|
Claude
|
True
|
NULL
|
monitor_2
|
NULL
|
NULL
|
NULL
|
NULL
|
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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
Show more
10:42
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Edit
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Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
Copy
Give positive feedback
Give negative feedback
Retry
You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
Retry
Edit
Copy
Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
Copy to clipboard
php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.
Copy
Give positive feedback
Give negative feedback
Retry
You said: Ok, so what will happen each time I make a request?
You said: Ok, so what will happen each time I make a request?
Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?
13:44
Retry
Edit
Copy
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Analyzed per-request overhead concerns and quantified Redis performance impact
Analyzed per-request overhead concerns and quantified Redis performance impact
Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out. But the overhead is much smaller than it sounds, and importantly, it's nowhere near your bottleneck.
What "two buckets per request" actually costs
What "two buckets per request" actually costs
Each request makes
one round trip to Redis
— not two — because you fold both bucket checks into a single Lua script that runs atomically server-side:
Copy to clipboard
lua
-- KEYS[1] = burst bucket key (or search)
-- KEYS[2] = daily bucket key
-- ARGV[1] = now (ms) ARGV[2] = window_ms
-- ARGV[3] = bucket_max ARGV[4] = daily_max
-- ARGV[5] = request_id ARGV[6] = daily_ttl
-- Trim sliding window
redis
.
call
(
'ZREMRANGEBYSCORE'
,
KEYS
[
1
]
,
0
,
ARGV
[
1
]
-
ARGV
[
2
]
)
local
burst_used
=
redis
.
call
(
'ZCARD'
,
KEYS
[
1
]
)
local
daily_used
=
tonumber
(
redis
.
call
(
'GET'
,
KEYS
[
2
]
)
or
'0'
)
if
burst_used
>=
tonumber
(
ARGV
[
3
]
)
then
-- Tell caller how long to sleep until oldest entry expires
local
oldest
=
redis
.
call
(
'ZRANGE'
,
KEYS
[
1
]
,
0
,
0
,
'WITHSCORES'
)
return
{
0
,
'BURST'
,
(
oldest
[
2
]
+
ARGV
[
2
]
)
-
ARGV
[
1
]
}
end
if
daily_used
>=
tonumber
(...
|
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That's the limit the 429 in your example is hitting (","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"secondly limit","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"on","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/crm/v3/objects/contact/search","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"). It's a separate bucket from the 190/10s, and critically, search responses do not include the","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-*","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"headers","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— you can't read remaining capacity off the response, you have to track it yourself.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":27,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":27,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Non-search endpoints return","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Max","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", and","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Interval-Milliseconds","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". The daily headers are not returned for OAuth-authenticated calls","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— that's enforced for marketplace listing, but it's also a sane operational target.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":27,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":27,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"So you have","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"four buckets","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Where to store it","depth":25,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Where to store it","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"INCR","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"EXPIRE","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"plus Lua scripting for true CAS semantics.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Key layout I'd use:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":28,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:search:{portalId} ZSET (sliding 1s)","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:meta:{portalId} HASH (last seen headers)","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The two short-window buckets are sliding-window-log (Redis ZSET, score =","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"microtime","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The daily bucket is a plain","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"INCR","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with TTL set to seconds-until-midnight in","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"HubSpot's account timezone","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to read it","depth":25,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to read it","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Two-phase: optimistic pre-check, authoritative post-update.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Before a call, run the Lua acquire script. If it returns \"denied,\" sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"After the call, parse","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Max","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Interval-Milliseconds","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and store them in","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:meta:{portalId}","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"For search specifically, since headers don't come back, the local counter","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"is","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"not","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"on 429 (you really did make that request).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to work with multiple jobs","depth":25,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to work with multiple jobs","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Concurrency cap","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"via","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Queue::throttle()","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Per-tenant fairness","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Priority lanes","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot-priority","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot-bulk","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Backoff on 429","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— respect the","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry-After","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"header. If absent, exponential with jitter (e.g.,","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"min(2^attempt * 250ms, 30s) ± 20%","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to work with paginated requests","depth":25,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to work with paginated requests","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The trap is treating \"fetch all\" as one logical operation. Each page is its own API call and competes for tokens with everything else.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Two practical rules:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Don't hold the worker idle while paginating.","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"processing","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"of page N as a separate job, and queue a \"fetch page N+1\" job with a delay equal to the wait time. Each page becomes its own atomic unit.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Always batch where HubSpot offers it.","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/{type}/batch/{read|update|create}","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"accepts up to 100 IDs per call. For your \"patching IDs\" flow this is the difference between 600 calls and 6.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Walkthrough: 600 opportunities","depth":25,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Walkthrough: 600 opportunities","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Sane flow:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Identify deltas","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— if you can avoid Search at all, do. Use","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Batch read","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"current state —","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/deals/batch/read","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with 100 IDs per call → 6 calls. Burst weight 6.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Compute diff","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"in your code (no API).","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Batch update","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"—","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/deals/batch/update","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with 100 per call → 6 calls.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Associations","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"—","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v4/associations/{from}/{to}/batch/create","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"if needed → another few batch calls.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Recognition: pre-check denies if","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"<weight>","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"slots aren't free; on 429 the response surfaces","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"policyName","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(DAILY/SECONDLY) — log it, push the job back with","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry-After","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"delay, increment a metric so you can alarm on sustained 429 rate.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Pitfalls & bottlenecks worth pre-empting","depth":25,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Pitfalls & bottlenecks worth pre-empting","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Daily quota is shared across the whole portal","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search is uniquely fragile","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":": separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hs_object_id","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"ranges or by","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"windows.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry storms after HubSpot incidents","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Webhook → sync → webhook loops","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"jy_last_sync_id","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":") and ignore webhooks where it just changed.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Token refresh","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"expires_in - 60s","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", single-flight the refresh (only one worker refreshes; others wait on a lock).","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search latency for fresh writes","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Clock skew","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— Redis is authoritative. Don't use","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"time()","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"from each worker for window scoring; use","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"redis.call('TIME')","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"inside the Lua script.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Dead workers with held tokens","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The 5% error rule","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Phantom contention with Laravel","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"RateLimited","depth":29,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"middleware","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":": it's per-job-class by default, not per-portal. 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Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? 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Tell caller how long to sleep until oldest entry expires","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"local","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"oldest","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"redis","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":".","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"call","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'ZRANGE'","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"KEYS","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"[","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"]","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"0","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"0","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'WITHSCORES'","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"return","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"{","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"0","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'BURST'","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"oldest","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"[","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"2","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"]","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"+","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"ARGV","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"[","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"2","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"]","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"ARGV","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"[","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"]","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
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Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
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You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
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Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
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┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
Copy to clipboard
php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.
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Retry
You said: Ok, so what will happen each time I make a request?
You said: Ok, so what will happen each time I make a request?
Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?
13:44
Retry
Edit
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Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Analyzed per-request overhead concerns and quantified Redis performance impact
Analyzed per-request overhead concerns and quantified Redis performance impact
Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out. But the overhead is much smaller than it sounds, and importantly, it's nowhere near your bottleneck.
What "two buckets per request" actually costs
What "two buckets per request" actually costs
Each request makes
one round trip to Redis
— not two — because you fold both bucket checks into a single Lua script that runs atomically server-side:
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lua
-- KEYS[1] = burst bucket key (or search)
-- KEYS[2] = daily bucket key
-- ARGV[1] = now (ms) ARGV[2] = window_ms
-- ARGV[3] = bucket_max ARGV[4] = daily_max
-- ARGV[5] = request_id ARGV[6] = daily_ttl
-- Trim sliding window
redis
.
call
(
'ZREMRANGEBYSCORE'
,
KEYS
[
1
]
,
0
,
ARGV
[
1
]
-
ARGV
[
2
]
)
local
burst_used
=
redis
.
call
(
'ZCARD'
,
KEYS
[
1
]
)
local
daily_used
=
tonumber
(
redis
.
call
(
'GET'
,
KEYS
[
2
]
)
or
'0'
)
if
burst_used
>=
tonumber
(
ARGV
[
3
]
)
then
-- Tell caller how long to sleep until oldest entry expires
local
oldest
=
redis
.
call
(
'ZRANGE'
,
KEYS
[
1
]
,
0
,
0
,
'WITHSCORES'
)
return
{
0
,
'BURST'
,
(
oldest
[
2
]
+
ARGV
[
2
]
)
-
ARGV
[
1
]
}
end
if
daily_used
>=
tonumber
(...
|
NULL
|
NULL
|
NULL
|
NULL
|
|
2627
|
110
|
28
|
2026-05-07T11:32:45.121238+00:00
|
/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778153565121_m2.jpg...
|
Claude
|
Claude
|
True
|
NULL
|
monitor_2
|
NULL
|
NULL
|
NULL
|
NULL
|
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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
Show more
10:42
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Copy
Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
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Give positive feedback
Give negative feedback
Retry
You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
Retry
Edit
Copy
Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
Copy to clipboard
php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.
Copy...
|
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That's the limit the 429 in your example is hitting (","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"secondly limit","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"on","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/crm/v3/objects/contact/search","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"). It's a separate bucket from the 190/10s, and critically, search responses do not include the","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-*","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"headers","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— you can't read remaining capacity off the response, you have to track it yourself.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":27,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":27,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Non-search endpoints return","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Max","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", and","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Interval-Milliseconds","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". The daily headers are not returned for OAuth-authenticated calls","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— that's enforced for marketplace listing, but it's also a sane operational target.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":27,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":27,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"So you have","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"four buckets","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Where to store it","depth":25,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Where to store it","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"INCR","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"EXPIRE","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"plus Lua scripting for true CAS semantics.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Key layout I'd use:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":28,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:search:{portalId} ZSET (sliding 1s)","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:meta:{portalId} HASH (last seen headers)","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The two short-window buckets are sliding-window-log (Redis ZSET, score =","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"microtime","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The daily bucket is a plain","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"INCR","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with TTL set to seconds-until-midnight in","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"HubSpot's account timezone","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to read it","depth":25,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to read it","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Two-phase: optimistic pre-check, authoritative post-update.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Before a call, run the Lua acquire script. If it returns \"denied,\" sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"After the call, parse","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Max","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Interval-Milliseconds","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and store them in","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:meta:{portalId}","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"For search specifically, since headers don't come back, the local counter","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"is","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"not","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"on 429 (you really did make that request).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to work with multiple jobs","depth":25,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to work with multiple jobs","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Concurrency cap","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"via","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Queue::throttle()","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Per-tenant fairness","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Priority lanes","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot-priority","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot-bulk","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Backoff on 429","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— respect the","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry-After","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"header. If absent, exponential with jitter (e.g.,","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"min(2^attempt * 250ms, 30s) ± 20%","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to work with paginated requests","depth":25,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to work with paginated requests","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The trap is treating \"fetch all\" as one logical operation. Each page is its own API call and competes for tokens with everything else.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Two practical rules:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Don't hold the worker idle while paginating.","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"processing","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"of page N as a separate job, and queue a \"fetch page N+1\" job with a delay equal to the wait time. Each page becomes its own atomic unit.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Always batch where HubSpot offers it.","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/{type}/batch/{read|update|create}","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"accepts up to 100 IDs per call. For your \"patching IDs\" flow this is the difference between 600 calls and 6.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Walkthrough: 600 opportunities","depth":25,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Walkthrough: 600 opportunities","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Sane flow:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Identify deltas","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— if you can avoid Search at all, do. Use","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Batch read","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"current state —","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/deals/batch/read","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with 100 IDs per call → 6 calls. Burst weight 6.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Compute diff","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"in your code (no API).","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Batch update","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"—","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/deals/batch/update","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with 100 per call → 6 calls.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Associations","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"—","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v4/associations/{from}/{to}/batch/create","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"if needed → another few batch calls.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Recognition: pre-check denies if","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"<weight>","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"slots aren't free; on 429 the response surfaces","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"policyName","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(DAILY/SECONDLY) — log it, push the job back with","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry-After","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"delay, increment a metric so you can alarm on sustained 429 rate.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Pitfalls & bottlenecks worth pre-empting","depth":25,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Pitfalls & bottlenecks worth pre-empting","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Daily quota is shared across the whole portal","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search is uniquely fragile","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":": separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hs_object_id","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"ranges or by","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"windows.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry storms after HubSpot incidents","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Webhook → sync → webhook loops","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"jy_last_sync_id","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":") and ignore webhooks where it just changed.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Token refresh","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"expires_in - 60s","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", single-flight the refresh (only one worker refreshes; others wait on a lock).","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search latency for fresh writes","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Clock skew","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— Redis is authoritative. Don't use","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"time()","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"from each worker for window scoring; use","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"redis.call('TIME')","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"inside the Lua script.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Dead workers with held tokens","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The 5% error rule","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Phantom contention with Laravel","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"RateLimited","depth":29,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"middleware","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":": it's per-job-class by default, not per-portal. If your","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"SyncDealJob","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"UpdateActivityJob","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"HubspotRateLimiter","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"service that everything routes through.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy","depth":23,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give positive feedback","depth":23,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give negative feedback","depth":23,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Retry","depth":23,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"You said: What will happen if there are two different types of rate limits?","depth":21,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"You said: What will happen if there are two different types of rate limits?","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"What will happen if there are two different types of rate limits?","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search requests that have 5 requests per second","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"13:40","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Retry","depth":23,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Edit","depth":23,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Copy","depth":23,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"Claude responded: They're independent buckets — with one shared bucket on top.","depth":22,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Claude responded: They're independent buckets — with one shared bucket on top.","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Analyzed independent rate limit buckets and their practical interactions","depth":23,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Analyzed independent rate limit buckets and their practical interactions","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"They're independent buckets — with one shared bucket on top.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"The three buckets and what each call hits","depth":25,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"The three buckets and what each call hits","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":28,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"┌──────────────────┐","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search request ──→ │ Search: 5/sec │ ──┐","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"└──────────────────┘ │","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"├──→ ┌─────────────────┐","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"┌──────────────────┐ │ │ Daily: 250k–1M │","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"└──────────────────┘ └─────────────────┘","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"What this means practically","depth":25,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"What this means practically","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting search secondly limit (5/s):","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Only further","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/search","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting burst limit (190/10s):","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting daily limit:","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"So can one type of request block the other?","depth":25,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"So can one type of request block the other?","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search → Burst:","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"No.","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Burst → Search:","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"No.","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Saturating burst leaves Search's 5/s untouched.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Either → Daily:","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Yes","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", but only by contributing to the same daily counter. It's volume that kills you here, not the type.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"In theory you can sustain ~50 search + ~190 other =","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"~240 calls per 10 seconds","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"indefinitely (until daily runs out), because the two buckets bill independently.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Implementation consequence","depth":25,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Implementation consequence","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":28,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"php","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'search'","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// for search endpoints only","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'burst'","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// for everything else","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'daily'","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// every call, both kinds","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('search')","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"+","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('daily')","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". A batch update calls","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('burst')","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"+","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('daily')","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":".","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"One subtle pitfall: when you backoff on a 429,","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"inspect","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"policyName","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"in the response body","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to know which bucket actually fired. If you see","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"SECONDLY","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The other subtlety: the burst response gives you","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy","depth":23,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false}]...
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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
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Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
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Retry
You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
Retry
Edit
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Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
Copy to clipboard
php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.
Copy...
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/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778153566577_m2.jpg...
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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
Show more
10:42
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Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
Copy
Give positive feedback
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Retry
You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
Retry
Edit
Copy
Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
Copy to clipboard
php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.
Copy
Give positive feedback
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Retry
You said: Ok, so what will happen each time I make a request?
You said: Ok, so what will happen each time I make a request?
Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?
13:44
Retry
Edit
Copy
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Analyzed per-request overhead concerns and quantified Redis performance impact
Analyzed per-request overhead concerns and quantified Redis performance impact
Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out. But the overhead is much smaller than it sounds, and importantly, it's nowhere near your bottleneck.
What "two buckets per request" actually costs
What "two buckets per request" actually costs
Each request makes
one round trip to Redis
— not two — because you fold both bucket checks into a single Lua script that runs atomically server-side:
Copy to clipboard
lua
-- KEYS[1] = burst bucket key (or search)
-- KEYS[2] = daily bucket key
-- ARGV[1] = now (ms) ARGV[2] = window_ms
-- ARGV[3] = bucket_max ARGV[4] = daily_max
-- ARGV[5] = request_id ARGV[6] = daily_ttl
-- Trim sliding window
redis
.
call
(
'ZREMRANGEBYSCORE'
,
KEYS
[
1
]
,
0
,
ARGV
[
1
]
-
ARGV
[
2
]
)
local
burst_used
=
redis
.
call
(
'ZCARD'
,
KEYS
[
1
]
)
local
daily_used
=
tonumber
(
redis
.
call
(
'GET'
,
KEYS
[
2
]
)
or
'0'
)
if
burst_used
>=
tonumber
(
ARGV
[
3
]
)
then
-- Tell caller how long to sleep until oldest entry expires
local
oldest
=
redis
.
call
(
'ZRANGE'
,
KEYS
[
1
]
,
0
,
0
,
'WITHSCORES'
)
return
{
0
,
'BURST'
,
(
oldest
[
2
]
+
ARGV
[
2
]
)
-
ARGV
[
1
]
}
end
if
daily_used
>=
tonumber
(
ARGV
[
4
]
)
then
return
{
0
,
'DAILY'
,
-
1
}
end
redis
.
call
(
'ZADD'
,
KEYS
[
1
]
,
ARGV
[
1
]
,
ARGV
[
5
]
)
redis
.
call
(
'PEXPIRE'
,
KEYS
[
1
]
,
ARGV
[
2
]
+
1000
)
local
d
=
redis
.
call
(
'INCR'
,
KEYS
[
2
]
)
if
d
==
1
then
redis
.
call
(
'EXPIRE'
,
KEYS
[
2
]
,
ARGV
[
6
]
)
end
return
{
1
,
'OK'
,
tonumber
(
ARGV
[
3
]
)
-
burst_used
-
1
}
One
EVALSHA
call. Two keys touched. Returns either
{1, OK, remaining}
or
{0, reason, retry_ms}
. No race conditions because Lua is single-threaded inside Redis. No "check then increment" gap that other workers can sneak through.
The math on whether this is heavy
The math on whether this is heavy
For your 100,000 requests, with 100 PATCHes batched into 1 batch_update call per 100 deals, you're realistically making more like
1,000–2,000 actual API calls
(assuming you're using
/batch/update
and
/batch/read
properly). But let's pretend you really make all 100k.
At HubSpot's ceiling of ~240 calls/sec across both buckets, 100k calls take roughly
7 minutes
of wall time. That means:
Redis ops/sec:
~240. A laptop-grade Redis instance handles 50,000+. You're using under half a percent.
Per-request overhead:
~0.3–1.0 ms for an
EVALSHA
against local-network Redis.
Per HubSpot call:
80–400 ms typical latency.
So the limiter adds something like
0.3% to each request's wall time
. The rate limit itself is forcing you to wait 4–40 ms between calls anyway; adding 0.5 ms of bookkeeping is invisible.
This is a pattern worth internalizing:
the rate limiter cannot be the bottleneck...
|
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Think carefully about the implementation and potential issue and bottlenecks.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Show more","depth":23,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"10:42","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Retry","depth":23,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Edit","depth":23,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Copy","depth":23,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"Claude responded: Current HubSpot limits (Apr 2026)","depth":22,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Claude responded: Current HubSpot limits (Apr 2026)","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms","depth":23,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Current HubSpot limits (Apr 2026)","depth":25,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Current HubSpot limits (Apr 2026)","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". The burst window is per app; the daily quota is per portal.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":27,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":27,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". That's the limit the 429 in your example is hitting (","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"secondly limit","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"on","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/crm/v3/objects/contact/search","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"). It's a separate bucket from the 190/10s, and critically, search responses do not include the","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-*","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"headers","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— you can't read remaining capacity off the response, you have to track it yourself.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":27,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":27,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Non-search endpoints return","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Max","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", and","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Interval-Milliseconds","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". The daily headers are not returned for OAuth-authenticated calls","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— that's enforced for marketplace listing, but it's also a sane operational target.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":27,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":27,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"So you have","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"four buckets","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Where to store it","depth":25,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Where to store it","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"INCR","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"EXPIRE","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"plus Lua scripting for true CAS semantics.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Key layout I'd use:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":28,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:search:{portalId} ZSET (sliding 1s)","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:meta:{portalId} HASH (last seen headers)","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The two short-window buckets are sliding-window-log (Redis ZSET, score =","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"microtime","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The daily bucket is a plain","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"INCR","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with TTL set to seconds-until-midnight in","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"HubSpot's account timezone","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to read it","depth":25,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to read it","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Two-phase: optimistic pre-check, authoritative post-update.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Before a call, run the Lua acquire script. If it returns \"denied,\" sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"After the call, parse","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Max","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Interval-Milliseconds","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and store them in","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:meta:{portalId}","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"For search specifically, since headers don't come back, the local counter","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"is","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"not","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"on 429 (you really did make that request).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to work with multiple jobs","depth":25,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to work with multiple jobs","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Concurrency cap","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"via","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Queue::throttle()","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Per-tenant fairness","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Priority lanes","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot-priority","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot-bulk","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Backoff on 429","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— respect the","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry-After","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"header. If absent, exponential with jitter (e.g.,","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"min(2^attempt * 250ms, 30s) ± 20%","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to work with paginated requests","depth":25,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to work with paginated requests","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The trap is treating \"fetch all\" as one logical operation. Each page is its own API call and competes for tokens with everything else.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Two practical rules:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Don't hold the worker idle while paginating.","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"processing","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"of page N as a separate job, and queue a \"fetch page N+1\" job with a delay equal to the wait time. Each page becomes its own atomic unit.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Always batch where HubSpot offers it.","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/{type}/batch/{read|update|create}","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"accepts up to 100 IDs per call. For your \"patching IDs\" flow this is the difference between 600 calls and 6.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Walkthrough: 600 opportunities","depth":25,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Walkthrough: 600 opportunities","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Sane flow:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Identify deltas","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— if you can avoid Search at all, do. Use","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Batch read","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"current state —","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/deals/batch/read","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with 100 IDs per call → 6 calls. Burst weight 6.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Compute diff","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"in your code (no API).","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Batch update","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"—","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/deals/batch/update","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with 100 per call → 6 calls.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Associations","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"—","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v4/associations/{from}/{to}/batch/create","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"if needed → another few batch calls.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Recognition: pre-check denies if","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"<weight>","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"slots aren't free; on 429 the response surfaces","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"policyName","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(DAILY/SECONDLY) — log it, push the job back with","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry-After","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"delay, increment a metric so you can alarm on sustained 429 rate.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Pitfalls & bottlenecks worth pre-empting","depth":25,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Pitfalls & bottlenecks worth pre-empting","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Daily quota is shared across the whole portal","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search is uniquely fragile","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":": separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hs_object_id","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"ranges or by","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"windows.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry storms after HubSpot incidents","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Webhook → sync → webhook loops","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"jy_last_sync_id","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":") and ignore webhooks where it just changed.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Token refresh","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"expires_in - 60s","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", single-flight the refresh (only one worker refreshes; others wait on a lock).","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search latency for fresh writes","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Clock skew","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— Redis is authoritative. Don't use","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"time()","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"from each worker for window scoring; use","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"redis.call('TIME')","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"inside the Lua script.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Dead workers with held tokens","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The 5% error rule","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Phantom contention with Laravel","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"RateLimited","depth":29,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"middleware","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":": it's per-job-class by default, not per-portal. 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Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"13:40","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Retry","depth":23,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Edit","depth":23,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Copy","depth":23,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"Claude responded: They're independent buckets — with one shared bucket on top.","depth":22,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Claude responded: They're independent buckets — with one shared bucket on top.","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Analyzed independent rate limit buckets and their practical interactions","depth":23,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Analyzed independent rate limit buckets and their practical interactions","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"They're independent buckets — with one shared bucket on top.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"The three buckets and what each call hits","depth":25,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"The three buckets and what each call hits","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":28,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"┌──────────────────┐","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search request ──→ │ Search: 5/sec │ ──┐","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"└──────────────────┘ │","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"├──→ ┌─────────────────┐","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"┌──────────────────┐ │ │ Daily: 250k–1M │","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"└──────────────────┘ └─────────────────┘","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"What this means practically","depth":25,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"What this means practically","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting search secondly limit (5/s):","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Only further","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/search","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting burst limit (190/10s):","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting daily limit:","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"So can one type of request block the other?","depth":25,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"So can one type of request block the other?","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search → Burst:","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"No.","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Burst → Search:","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"No.","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Saturating burst leaves Search's 5/s untouched.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Either → Daily:","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Yes","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", but only by contributing to the same daily counter. It's volume that kills you here, not the type.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"In theory you can sustain ~50 search + ~190 other =","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"~240 calls per 10 seconds","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"indefinitely (until daily runs out), because the two buckets bill independently.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Implementation consequence","depth":25,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Implementation consequence","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":28,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"php","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'search'","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// for search endpoints only","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'burst'","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// for everything else","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'daily'","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// every call, both kinds","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('search')","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"+","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('daily')","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". A batch update calls","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('burst')","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"+","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('daily')","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":".","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"One subtle pitfall: when you backoff on a 429,","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"inspect","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"policyName","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"in the response body","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to know which bucket actually fired. If you see","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"SECONDLY","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The other subtlety: the burst response gives you","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy","depth":23,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give positive feedback","depth":23,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give negative feedback","depth":23,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Retry","depth":23,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"You said: Ok, so what will happen each time I make a request?","depth":21,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"You said: Ok, so what will happen each time I make a request?","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"13:44","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Retry","depth":23,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Edit","depth":23,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Copy","depth":23,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.","depth":22,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Analyzed per-request overhead concerns and quantified Redis performance impact","depth":23,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Analyzed per-request overhead concerns and quantified Redis performance impact","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out. But the overhead is much smaller than it sounds, and importantly, it's nowhere near your bottleneck.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"What \"two buckets per request\" actually costs","depth":25,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"What \"two buckets per request\" actually costs","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Each request makes","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"one round trip to Redis","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— not two — because you fold both bucket checks into a single Lua script that runs atomically server-side:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to 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Two keys touched. Returns either","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"{1, OK, remaining}","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"or","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"{0, reason, retry_ms}","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". No race conditions because Lua is single-threaded inside Redis. No \"check then increment\" gap that other workers can sneak through.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"The math on whether this is heavy","depth":25,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"The math on whether this is heavy","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"For your 100,000 requests, with 100 PATCHes batched into 1 batch_update call per 100 deals, you're realistically making more like","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1,000–2,000 actual API calls","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(assuming you're using","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/batch/update","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/batch/read","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"properly). But let's pretend you really make all 100k.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"At HubSpot's ceiling of ~240 calls/sec across both buckets, 100k calls take roughly","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"7 minutes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"of wall time. That means:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Redis ops/sec:","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"~240. A laptop-grade Redis instance handles 50,000+. You're using under half a percent.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Per-request overhead:","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"~0.3–1.0 ms for an","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"EVALSHA","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"against local-network Redis.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Per HubSpot call:","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"80–400 ms typical latency.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"So the limiter adds something like","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"0.3% to each request's wall time","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". The rate limit itself is forcing you to wait 4–40 ms between calls anyway; adding 0.5 ms of bookkeeping is invisible.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"This is a pattern worth internalizing:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"the rate limiter cannot be the bottleneck","depth":27,"on_screen":false,"role_description":"text"}]...
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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
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Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
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You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
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Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
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┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
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php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.
Copy
Give positive feedback
Give negative feedback
Retry
You said: Ok, so what will happen each time I make a request?
You said: Ok, so what will happen each time I make a request?
Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?
13:44
Retry
Edit
Copy
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Analyzed per-request overhead concerns and quantified Redis performance impact
Analyzed per-request overhead concerns and quantified Redis performance impact
Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out. But the overhead is much smaller than it sounds, and importantly, it's nowhere near your bottleneck.
What "two buckets per request" actually costs
What "two buckets per request" actually costs
Each request makes
one round trip to Redis
— not two — because you fold both bucket checks into a single Lua script that runs atomically server-side:
Copy to clipboard
lua
-- KEYS[1] = burst bucket key (or search)
-- KEYS[2] = daily bucket key
-- ARGV[1] = now (ms) ARGV[2] = window_ms
-- ARGV[3] = bucket_max ARGV[4] = daily_max
-- ARGV[5] = request_id ARGV[6] = daily_ttl
-- Trim sliding window
redis
.
call
(
'ZREMRANGEBYSCORE'
,
KEYS
[
1
]
,
0
,
ARGV
[
1
]
-
ARGV
[
2
]
)
local
burst_used
=
redis
.
call
(
'ZCARD'
,
KEYS
[
1
]
)
local
daily_used
=
tonumber
(
redis
.
call
(
'GET'
,
KEYS
[
2
]
)
or
'0'
)
if
burst_used
>=
tonumber
(
ARGV
[
3
]
)
then
-- Tell caller how long to sleep until oldest entry expires
local
oldest
=
redis
.
call
(
'ZRANGE'
,
KEYS
[
1
]
,
0
,
0
,
'WITHSCORES'
)
return
{
0
,
'BURST'
,
(
oldest
[
2
]
+
ARGV
[
2
]
)
-
ARGV
[
1
]
}
end
if
daily_used
>=
tonumber
(
ARGV
[
4
]
)
then
return
{
0
,
'DAILY'
,
-
1
}
end
redis
.
call
(
'ZADD'
,
KEYS
[
1
]
,
ARGV
[
1
]
,
ARGV
[
5
]
)
redis
.
call
(
'PEXPIRE'
,
KEYS
[
1
]
,
ARGV
[
2
]
+
1000
)
local
d
=
redis
.
call
(
'INCR'
,
KEYS
[
2
]
)
if
d
==
1
then
redis
.
call
(
'EXPIRE'
,
KEYS
[
2
]
,
ARGV
[
6
]
)
end
return
{
1
,
'OK'
,
tonumber
(
ARGV
[
3
]
)
-
burst_used
-
1
}
One
EVALSHA
call. Two keys touched. Returns either
{1, OK, remaining}
or
{0, reason, retry_ms}
. No race conditions because Lua is single-threaded inside Redis. No "check then increment" gap that other workers can sneak through.
The math on whether this is heavy
The math on whether this is heavy
For your 100,000 requests, with 100 PATCHes batched into 1 batch_update call per 100 deals, you're realistically making more like
1,000–2,000 actual API calls
(assuming you're using
/batch/update
and
/batch/read
properly). But let's pretend you really make all 100k.
At HubSpot's ceiling of ~240 calls/sec across both buckets, 100k calls take roughly
7 minutes
of wall time. That means:
Redis ops/sec:
~240. A laptop-grade Redis instance handles 50,000+. You're using under half a percent.
Per-request overhead:
~0.3–1.0 ms for an
EVALSHA
against local-network Redis.
Per HubSpot call:
80–400 ms typical latency.
So the limiter adds something like
0.3% to each request's wall time
. The rate limit itself is forcing you to wait 4–40 ms between calls anyway; adding 0.5 ms of bookkeeping is invisible.
This is a pattern worth internalizing:
the rate limiter cannot be the bottleneck...
|
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2630
|
110
|
30
|
2026-05-07T11:32:50.755342+00:00
|
/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778153570755_m2.jpg...
|
PhpStorm
|
faVsco.js – Hubspot/Client.php
|
True
|
NULL
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monitor_2
|
NULL
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NULL
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NULL
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NULL
|
Project: faVsco.js, menu
master, menu
Start Listen Project: faVsco.js, menu
master, menu
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# [PASSWORD_DOTS] HS [PASSWORD_DOTS]
select * from teams where id = 2; # 2
select * from features; # 2
select * from team_features where team_id = 2; # 2
select * from crm_configurations where id = 2; # 2
select * from users where team_id = 2; #
select * from playbooks where team_id = 2; # event 38
select * from playbook_categories where playbook_id = 38; #
SELECT * FROM activities WHERE crm_configuration_id = 2 and crm_provider_id is not null order by id desc;
[URL_WITH_CREDENTIALS] CrmFieldOption array{id:string, label:string, value?:string}
*/
class Client extends BaseClient implements HubspotClientInterface
{
public const string MIN_API_VERSION = '2';
public const string BASE_URL = '[URL_WITH_CREDENTIALS] T
* @param callable(): T $apiCall
* @return T
*
* @throws RateLimitException
*/
private function executeRequest(callable $apiCall)
{
if (! $this->rateLimiter->canMakeRequest($this->config)) {
$retryAfter = $this->rateLimiter->requestAvailableIn($this->config);
$this->log->warning('[Hubspot] Rate limit exceeded, deferring request', [
'team_id' => $this->config->team_id,
'config_id' => $this->config->getId(),
'retry_after' => $retryAfter,
]);
throw new RateLimitException(
'Hubspot rate limit reached for configuration ' . $this->config->getId(),
$retryAfter,
);
}
$this->rateLimiter->incrementRequestCount($this->config);
try {
return $apiCall();
} catch (Throwable $e) {
if ($this->isHubspotRateLimit($e)) {
$retryAfter = $this->parseRetryAfter($e);
$this->log->warning('[Hubspot] Received 429 from API', [
'team_id' => $this->config->team_id,
'config_id' => $this->config->getId(),
'retry_after' => $retryAfter,
'reason' => $e->getMessage(),
]);
throw new RateLimitException('Hubspot returned 429', $retryAfter, $e);
}
throw $e;
}
}
private function isHubspotRateLimit(Throwable $e): bool
{
return method_exists($e, 'getCode') && (int) $e->getCode() === 429;
}
private function parseRetryAfter(Throwable $e): int
{
if (method_exists($e, 'getResponseHeaders')) {
$headers = $e->getResponseHeaders() ?: [];
$value = $headers['Retry-After'] ?? $headers['retry-after'] ?? null;
if (is_array($value)) {
$value = $value[0] ?? null;
}
if (is_numeric($value)) {
return (int) $value;
}
}
return 10;
}
public function getMinimumApiVersion(): string
{
return self::MIN_API_VERSION;
}
public function getInstance(): Factory
{
return new Factory([
'key' => $this->accessToken,
'oauth2' => true,
'base_url' => $this->baseUrl,
]);
}
public function getNewInstance(): Discovery
{
return \HubSpot\Factory::createWithAccessToken($this->accessToken);
}
/**
* Secondly and daily limits for Hubspot API
*
* Product Tier: Free & Starter | Professional & Enterprise | API add-on (any tier)
* Burst: 100/10 seconds | 150/10 seconds | 200/10 seconds
* Daily: 250,000 | 500,000 | 1,000,000
*
* Official documentation states: The search endpoints are rate limited to five requests per second.
* Since with 5 RPS were still hitting secondly rate limits we lowered it to 4
*/
public function getPaginatedData(array $payload, string $type, int $offset = 0): array
{
$total = 0;
$lastId = null;
$rows = [];
foreach ($this->getPaginatedDataGenerator($payload, $type, $offset, $total, $lastId) as $row) {
$rows[] = $row;
}
return ['results' => $rows, 'total' => $total, 'last_record' => $lastId];
}
/**
* @throws HubspotException
* @throws SocialAccountTokenInvalidException
* @throws BadRequest
*/
public function getPaginatedDataGenerator(
array $payload,
string $type,
int $offset = 0,
int &$total = 0,
?string &$lastRecordId = null
): \Generator {
return $this->paginationService->getPaginatedDataGenerator(
$this,
$payload,
$type,
$offset,
$total,
$lastRecordId
);
}
/**
* @throws DealApiException
* @throws CrmException
*/
public function getOpportunityById(string $crmId, array $fields): array
{
try {
$deal = $this->executeRequest(fn () => $this->getNewInstance()->crm()->deals()->basicApi()->getById(
$crmId,
implode(',', $fields),
'companies,contacts'
));
} catch (DealApiException $e) {
$this->log->info('[Hubspot] Failed to fetch opportunity', [
'crm_id' => $crmId,
'reason' => $e->getMessage(),
]);
throw $e;
}
if (! $deal instanceof DealWithAssociations) {
throw new CrmException('Deal not found');
}
return [
'id' => $deal->getId(),
'properties' => $deal->getProperties(),
'associations' => $deal->getAssociations(),
];
}
/**
* Generic batch read method for HubSpot objects
*
* @param string $objectType The object type ('deals', 'companies', 'contacts')
* @param array<string> $crmIds Array of HubSpot object IDs (max 100)
* @param array<string> $fields Array of property names to fetch
*
* @return array<string, array> Array keyed by CRM ID with object data
*/
private function batchReadObjects(string $objectType, array $crmIds, array $fields): array
{
if (empty($crmIds)) {
return [];
}
$this->validateBatchSize($objectType, $crmIds);
$this->ensureValidToken();
try {
$batchConfig = $this->createBatchConfiguration($objectType);
$batchReadRequest = $this->prepareBatchRequest($batchConfig, $crmIds, $fields);
$response = $batchConfig['api']->read($batchReadRequest);
$this->validateApiResponse($response, $objectType);
$results = $this->processApiResults($response);
$this->logBatchResults($objectType, $crmIds, $results);
return $results;
} catch (\Throwable $e) {
$this->handleBatchError($e, $objectType, $crmIds);
}
}
private function validateBatchSize(string $objectType, array $crmIds): void
{
if (count($crmIds) > 100) {
throw new \InvalidArgumentException("Batch size cannot exceed 100 {$objectType}");
}
}
private function createBatchConfiguration(string $objectType): array
{
$configurations = [
'deals' => [
'batchReadRequest' => new \HubSpot\Client\Crm\Deals\Model\BatchReadInputSimplePublicObjectId(),
'inputClass' => \HubSpot\Client\Crm\Deals\Model\SimplePublicObjectId::class,
'api' => $this->getNewInstance()->crm()->deals()->batchApi(),
],
'companies' => [
'batchReadRequest' => new \HubSpot\Client\Crm\Companies\Model\BatchReadInputSimplePublicObjectId(),
'inputClass' => \HubSpot\Client\Crm\Companies\Model\SimplePublicObjectId::class,
'api' => $this->getNewInstance()->crm()->companies()->batchApi(),
],
'contacts' => [
'batchReadRequest' => new \HubSpot\Client\Crm\Contacts\Model\BatchReadInputSimplePublicObjectId(),
'inputClass' => \HubSpot\Client\Crm\Contacts\Model\SimplePublicObjectId::class,
'api' => $this->getNewInstance()->crm()->contacts()->batchApi(),
],
];
if (! isset($configurations[$objectType])) {
throw new \InvalidArgumentException("Unsupported object type: {$objectType}");
}
return $configurations[$objectType];
}
private function prepareBatchRequest(array $batchConfig, array $crmIds, array $fields): object
{
$batchReadRequest = $batchConfig['batchReadRequest'];
$inputClass = $batchConfig['inputClass'];
$inputs = array_map(function ($crmId) use ($inputClass) {
$input = new $inputClass();
$input->setId($crmId);
return $input;
}, $crmIds);
$batchReadRequest->setInputs($inputs);
$batchReadRequest->setProperties($fields);
return $batchReadRequest;
}
private function validateApiResponse($response, string $objectType): void
{
if (! $response) {
throw new CrmException("HubSpot API returned null response for {$objectType} batch read");
}
}
private function processApiResults($response): array
{
$results = [];
$responseResults = $response->getResults();
if ($responseResults) {
foreach ($responseResults as $object) {
if ($object && $object->getId()) {
$results[$object->getId()] = [
'id' => $object->getId(),
'properties' => $object->getProperties() ?: [],
];
}
}
}
return $results;
}
private function logBatchResults(string $objectType, array $crmIds, array $results): void
{
$this->log->info("[HubSpot] Batch fetched {$objectType}", [
'requested_count' => count($crmIds),
'returned_count' => count($results),
'crm_ids' => $crmIds,
]);
}
private function handleBatchError(\Throwable $e, string $objectType, array $crmIds): void
{
$errorMessage = $e->getMessage() ?: 'Unknown error';
$errorTrace = $e->getTraceAsString() ?: 'No trace available';
$this->log->error("[HubSpot] Failed to batch fetch {$objectType}", [
'crm_ids' => $crmIds,
'error' => $errorMessage,
'trace' => $errorTrace,
]);
throw new CrmException("Failed to batch fetch {$objectType}: " . $errorMessage);
}
/**
* Batch read multiple opportunities by their CRM IDs
*
* @param array<string> $crmIds Array of HubSpot deal IDs (max 100)
* @param array<string> $fields Array of property names to fetch
*
* @return array<string, array> Array keyed by CRM ID with opportunity data
*/
public function getOpportunitiesByIds(array $crmIds, array $fields): array
{
return $this->batchReadObjects('deals', $crmIds, $fields);
}
/**
* Batch read multiple companies by their CRM IDs
*
* @param array<string> $crmIds Array of HubSpot company IDs (max 100)
* @param array<string> $fields Array of property names to fetch
*
* @return array<string, array> Array keyed by CRM ID with company data
*/
public function getCompaniesByIds(array $crmIds, array $fields): array
{
return $this->batchReadObjects('companies', $crmIds, $fields);
}
/**
* Batch read multiple contacts by their CRM IDs
*
* @param array<string> $crmIds Array of HubSpot contact IDs (max 100)
* @param array<string> $fields Array of property names to fetch
*
* @return array<string, array> Array keyed by CRM ID with contact data
*/
public function getContactsByIds(array $crmIds, array $fields): array
{
return $this->batchReadObjects('contacts', $crmIds, $fields);
}
/**
* @throws CompanyApiException
* @throws CrmException
*/
public function getAccountById(string $crmId, array $fields): array
{
try {
$company = $this->getNewInstance()->crm()->companies()->basicApi()->getById(
$crmId,
implode(',', $fields),
);
} catch (CompanyApiException $e) {
$this->log->info('[Hubspot] Failed to fetch account', [
'crm_id' => $crmId,
'reason' => $e->getMessage(),
]);
throw $e;
}
if (! $company instanceof CompaniesWithAssociations) {
throw new CrmException('Account not found');
}
return [
'id' => $company->getId(),
'properties' => $company->getProperties(),
];
}
/**
* @throws ContactApiException
* @throws CrmException
*/
public function getContactById(string $crmId, array $fields): array
{
try {
$contact = $this->getNewInstance()->crm()->contacts()->basicApi()->getById(
$crmId,
implode(',', $fields)
);
} catch (ContactApiException $e) {
$this->log->info('[Hubspot] Failed to fetch contact', [
'crm_id' => $crmId,
'reason' => $e->getMessage(),
]);
throw $e;
}
if (! $contact instanceof ContactsWithAssociations) {
throw new CrmException('Contact not found');
}
return [
'id' => $contact->getId(),
'properties' => $contact->getProperties(),
];
}
/**
* This is email search request that Hubspot offers as GET (more generous quota)
*/
public function getContactByEmail(string $email, array $fields = []): array
{
try {
$contact = $this->getNewInstance()->crm()->contacts()->basicApi()->getById(
$email,
implode(',', $fields),
null,
false,
'email'
);
return [
'id' => $contact->getId(),
'properties' => $contact->getProperties(),
];
} catch (ContactApiException $e) {
$this->log->info('[Hubspot] Failed to fetch contact', [
'email' => $email,
'reason' => $e->getMessage(),
]);
return [];
}
}
/**
* @throws CrmException
*/
public function fetchProperty(string $objectType, string $propertyId): Property
{
$result = $this->getNewInstance()->crm()->properties()->coreApi()->getByName($objectType, $propertyId);
if (! $result instanceof Property) {
$this->log->error('[Hubspot] Failed to fetch property', [
'object_type' => $objectType,
'property_id' => $propertyId,
'reason' => $result->getMessage(),
]);
throw new CrmException('Failed to fetch property');
}
return $result;
}
/**
* @return array<CrmFieldOption>
*/
public function fetchPropertyOptions(string $objectType, string $propertyId): array
{
/** @var array<CrmFieldOption> */
return $this->fetchProperty($objectType, $propertyId)->getOptions();
}
/**
* @return array<array{id:string, label:string, deleted:bool}>
*/
public function fetchCallDispositions(): array
{
/** @var Response $response */
$response = $this->getInstance()->engagements()->getCallDispositions();
/**
* @var array<array{
* id:string,
* label:string,
* deleted: bool
* }>
*/
return $response->toArray();
}
/**
* @return array<CrmFieldOption>
*/
public function fetchOpportunityPipelineStages(): array
{
$stages = [];
$apiResponse = $this->getNewInstance()->crm()->pipelines()->pipelinesApi()->getAll('deals');
if ($apiResponse instanceof Error) {
$this->log->error('[Hubspot] Failed to fetch opportunity pipelines', [
'reason' => $apiResponse->getMessage(),
]);
return [];
}
foreach ($apiResponse->getResults() as $pipeline) {
$pipelineStages = array_map(
static function (PipelineStage $stage) {
return [
'id' => $stage->getId(),
'label' => $stage->getLabel(),
];
},
$pipeline->getStages()
);
$stages = array_merge($stages, $pipelineStages);
}
return $stages;
}
public function fetchOpportunityPipelines(): array
{
$pipelines = [];
try {
$apiResponse = $this->makeRequest('/crm/v3/pipelines/deals');
} catch (\Exception $e) {
$this->log->info('[Hubspot] Failed to fetch opportunity pipelines', [
'reason' => $e->getMessage(),
]);
return [];
}
$response = $apiResponse->toArray();
foreach ($response['results'] as $pipeline) {
$pipelines[] = [
'id' => $pipeline['id'],
'label' => $pipeline['label'],
];
}
return $pipelines;
}
/**
* @return array<CrmFieldOption>
*/
public function fetchMeetingOutcomeFieldOptions(Field $field): array
{
return $field->getCrmProviderId() === 'meetingOutcome'
? $this->fetchMeetingOutcomeTypes()
: $this->fetchCallActivityTypes();
}
public function fetchMeetingOutcomeTypes(): array
{
return $this->extractMeetingTypeOptions(
'[URL_WITH_CREDENTIALS] Response $response */
$response = $this->getInstance()
->getClient()
->request('GET', $endpoint);
/**
* @var array<array{
* value: string,
* label: string,
* displayOrder: int
* }> $optionData
*/
$optionData = $response->toArray()['options'] ?? [];
$options = [];
foreach ($optionData as $item) {
$options[] = [
'id' => $item['value'],
'value' => $item['value'],
'label' => $item['label'],
'display_order' => $item['displayOrder'],
];
}
return $options;
}
/**
* @return array<CrmFieldOption>
*/
public function fetchDispositionFieldOptions(): array
{
$options = [];
$dispositions = $this->fetchCallDispositions();
foreach ($dispositions as $disposition) {
if ($disposition['deleted'] !== false) {
continue;
}
$option['value'] = $disposition['id'];
$option['id'] = $disposition['id'];
$option['label'] = $disposition['label'];
$options[] = $option;
}
return $options;
}
/**
* @return array<CrmFieldOption>
*/
public function fetchOpportunityFieldOptions(Field $field): array
{
if ($field->isStageField()) {
return $this->fetchOpportunityPipelineStages();
}
if ($field->isPipelineField()) {
return $this->fetchOpportunityPipelines();
}
return $this->fetchPropertyOptions('deals', $field->getCrmProviderId());
}
/**
* @throws BadRequest
* @throws HubspotException
*/
public function makeRequest(string $endpoint, $method = 'GET', $payload = [], ?string $queryString = null)
{
$endpoint = self::BASE_URL . $endpoint;
if ($method === 'GET') {
return $this->getInstance()->getClient()?->request(
method: $method,
endpoint: $endpoint,
query_string: $queryString
);
} else {
return $this->getInstance()->getClient()->request($method, $endpoint, [
'json' => ($payload),
]);
}
}
/**
* @throws BadRequest
* @throws HubspotException
*/
public function createMeeting(array $payload): Response
{
$endpoint = '/crm/v3/objects/meetings';
return $this->makeRequest($endpoint, 'POST', $payload);
}
/**
* @throws BadRequest
* @throws HubspotException
*/
public function updateMeeting(string $meetingId, array $payload): Response
{
$endpoint = '/crm/v3/objects/meetings/' . $meetingId;
return $this->makeRequest($endpoint, 'PATCH', $payload);
}
/**
* @throws \Exception
*/
public function createNote(
string $body,
string $ownerId,
int $timestamp,
string $objectId,
NoteObject $noteObject
): ?string {
try {
$noteInput = new SimplePublicObjectInput([
'properties' => [
'hs_note_body' => $body,
'hubspot_owner_id' => $ownerId,
'hs_timestamp' => $timestamp,
],
]);
// Create note
$note = $this->getNewInstance()->crm()->objects()->basicApi()->create('note', $noteInput);
$this->getNewInstance()->crm()->objects()->associationsApi()->create(
'note',
$note->getId(),
$this->getNoteObject($noteObject),
$objectId,
$this->getNoteAssociationType($noteObject),
);
return $note->getId();
} catch (\Exception $e) {
$this->log->error('[Hubspot] Failed to create note', [
'objectId' => $objectId,
'noteObject' => $noteObject->getObjectType(),
'reason' => $e->getMessage(),
]);
\Sentry::captureException($e);
}
return null;
}
public function updateEngagement(string $objectId, array $engagement, array $metadata): void
{
$this->getInstance()->engagements()->update($objectId, $engagement, $metadata);
}
public function getEngagementData(string $engagementId): array
{
$engagement = $this->getInstance()->engagements()->get($engagementId);
return $engagement->toArray();
}
public function createEngagement(array $engagement, array $associations, array $metadata): Response
{
return $this->getInstance()
->engagements()
->create($engagement, $associations, $metadata);
}
public function isUnauthorizedException(\Exception $e): bool
{
// Check for specific HubSpot API exception types first
if ($e instanceof BadRequest) {
// BadRequest can contain 401 status codes
return $e->getCode() === 401;
}
// Check for HTTP client exceptions with status codes
if ($e instanceof \GuzzleHttp\Exception\RequestException && $e->hasResponse()) {
$response = $e->getResponse();
if ($response !== null) {
return $response->getStatusCode() === 401;
}
}
// Check for Guzzle HTTP exceptions
if ($e instanceof \GuzzleHttp\Exception\ClientException) {
return $e->getCode() === 401;
}
// Fallback to string matching as last resort, but be more specific
$message = strtolower($e->getMessage());
return str_contains($message, '401 unauthorized') ||
str_contains($message, 'http 401') ||
str_contains($message, 'status code 401') ||
(preg_match('/\b401\b/', $message) && str_contains($message, 'unauthorized'));
}
/**
* Validates and refreshes the access token if needed before API requests.
* This ensures long-running processes don't fail due to token expiration.
*
* @throws SocialAccountTokenInvalidException
*/
public function ensureValidToken(): void
{
if ($this->oauthAccount === null) {
return;
}
$newToken = $this->tokenManager->ensureValidToken($this->oauthAccount);
if ($newToken !== null) {
$this->accessToken = $newToken;
}
}
public function getConfig()
{
return $this->config;
}
// returns only active (archived=false)
public function getOwners(): array
{
return $this->getNewInstance()->crm()->owners()->getAll();
}
/**
* @param bool $archived
*
* @return array<Owner>|[]
*/
public function getOwnersArchived(bool $archived = true): array
{
$endpoint = '/crm/v3/owners';
$queryParams = [
'archived' => $archived ? 'true' : 'false',
];
$queryString = http_build_query($queryParams);
$owners = [];
try {
$response = $this->makeRequest(endpoint: $endpoint, queryString: $queryString);
$responseData = $response?->toArray();
foreach ($responseData['results'] as $result) {
try {
$owners[] = Owner::create($result);
} catch (Throwable $e) {
$this->log->error('[HubSpot] Failed to process owner data', [
'result' => $result,
'error' => $e->getMessage(),
]);
continue;
}
}
} catch (Throwable $e) {
$this->log->error('HubSpot] Failed to fetch owners', [
'archived' => $archived,
'error' => $e->getMessage(),
]);
return [];
}
return $owners;
}
public function getMeeting(string $engagementId): ObjectWithAssociations
{
return $this->getNewInstance()->crm()->objects()->basicApi()
->getById('meeting', $engagementId, null, 'contact,company,deal');
}
public function deleteEngagement(string $engagementId): void
{
$this->getInstance()->engagements()->delete((int) $engagementId);
}
public function getAssociationsData(array $ids, string $fromObject, string $toObject): array
{
$associationData = [];
$idChunks = array_chunk($ids, self::ASSOCIATIONS_BATCH_SIZE_LIMIT);
foreach ($idChunks as $idChunk) {
try {
$batchInput = new \HubSpot\Client\Crm\Associations\Model\BatchInputPublicObjectId();
$batchInput->setInputs(array_map(function ($id) {
$publicObjectId = new \HubSpot\Client\Crm\Associations\Model\PublicObjectId();
$publicObjectId->setId($id);
return $publicObjectId;
}, $idChunk));
$associatedObjectsData = $this
->getNewInstance()
->crm()
->associations()
->batchApi()
->read($fromObject, $toObject, $batchInput);
if ($associatedObjectsData instanceof \HubSpot\Client\Crm\Associations\Model\BatchResponsePublicAssociationMulti) {
foreach ($associatedObjectsData->getResults() as $association) {
$from = $association->getFrom()->getId();
$toAssociations = $association->getTo();
if (! empty($toAssociations)) {
$associationData[$from] = array_map(function ($item) {
return $item->getId();
}, $toAssociations);
}
}
}
} catch (\Exception $e) {
$this->log->error('[Hubspot] Failed to fetch associations', [
'from_object' => $fromObject,
'to_object' => $toObject,
'reason' => $e->getMessage(),
]);
}
}
return $associationData;
}
/**
* @throws \Exception
*/
private function getNoteAssociationType(NoteObject $noteObject): string
{
return match($noteObject) {
NoteObject::Opportunity => 'note_to_deal',
NoteObject::Lead, NoteObject::Contact => 'note_to_contact', // or 'note_to_lead' if your portal supports it
NoteObject::Account => 'note_to_company',
NoteObject::Call, NoteObject::Event => throw new \Exception('Not supported'),
};
}
/**
* @throws \Exception
*/
private function getNoteObject(NoteObject $noteObject): string
{
return match($noteObject) {
NoteObject::Opportunity => 'deal',
NoteObject::Lead, NoteObject::Contact => 'contact',
NoteObject::Account => 'company',
NoteObject::Call, NoteObject::Event => throw new \Exception('Not supported'),
};
}
public function addAssociations(string $objectType, string $associationType, array $payload): Response
{
$endpoint = "/crm/v4/associations/$objectType/$associationType/batch/create";
return $this->makeRequest($endpoint, 'POST', $payload);
}
public function removeAssociations(string $objectType, string $associationType, array $payload): Response
{
$endpoint = "/crm/v4/associations/$objectType/$associationType/batch/archive";
return $this->makeRequest($endpoint, 'POST', $payload);
}
}
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Highlighted Error","depth":4,"bounds":{"left":0.66888297,"top":0.12210695,"width":0.00731383,"height":0.018355945},"on_screen":true,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Next Highlighted Error","depth":4,"bounds":{"left":0.6761968,"top":0.12210695,"width":0.006981383,"height":0.018355945},"on_screen":true,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXTextArea","text":"# **************************** HS **************************************\n\nselect * from teams where id = 2; # 2\nselect * from features; # 2\nselect * from team_features where team_id = 2; # 2\nselect * from crm_configurations where id = 2; # 2\nselect * from users where team_id = 2; #\nselect * from playbooks where team_id = 2; # event 38\nselect * from playbook_categories where playbook_id = 38; #\n\nSELECT * FROM activities WHERE crm_configuration_id = 2 and crm_provider_id is not null order by id desc;\nhttps://app.hubspot.com/contacts/4392066/deal/16964514951/?engagement=96069102624\n https://app.staging.jiminny.com/playback/d5df34dc-bd66-4ff5-a7b3-8d3be30322a0\n\nSELECT * FROM activities WHERE uuid_to_bin('04fdcd0d-818f-4c53-92dc-6f18bc753ffd') = uuid;\n# 609126 softphone tr. 11241\n\nSELECT * FROM activities WHERE uuid_to_bin('6521bfcd-5a30-46e5-9f74-5440fd48befd') = uuid;\n# 608874 conference tr. 11226 crmId: 103422236596\n\nselect * from ai_prompts where transcription_id IN (11241, 11226);\nselect * from activity_summary_logs where activity_id = 608874;\n\nselect * from sidekick_settings;\nselect * from default_activity_types;\n\nselect * from crm_field_data where activity_id = 1223;\n\nselect * from crm_layouts where crm_configuration_id = 2;\nSELECT * FROM crm_layout_entities WHERE crm_layout_id IN (554);\nselect * from crm_fields where crm_configuration_id = 11 and object_type = 'event';\nSELECT * FROM crm_field_values WHERE crm_field_id IN (1455,1450);\n\nSELECT * FROM crm_field_data WHERE crm_layout_entity_id = 971;\nSELECT * FROM crm_field_data WHERE crm_layout_entity_id IN (6494,6495,6496,6497,6498,6499);\n\nSELECT\n CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,\n u.email,\n sa.*,\n t.owner_id FROM social_accounts sa\nJOIN users u\n on u.id = sa.sociable_id\nJOIN teams t on t.id = u.team_id\nWHERE u.team_id = 2 and sa.provider = 'hubspot';\n\nselect * from social_accounts where id = 1499;\n\nselect * from opportunities where team_id = 2\nand crm_provider_id IN ('51317301383');\n\nselect * from contacts where id = 85;\n\nselect * from opportunities where team_id = 2 order by id desc;\nselect * from opportunities where team_id = 2 and crm_provider_id = '51317301383'; # 5112\nselect * from opportunities where team_id = 2 and crm_provider_id = '55976759904'; # 5112\nselect * from opportunity_contacts where opportunity_id = 5117;\nselect * from crm_field_data where object_id = 1365;\nSELECT * FROM crm_fields WHERE id IN (1405, 1407, 1972, 2128);\n\nselect * from features;\nselect * from team_features where team_id IN (1);\nselect * from team_features where feature_id IN (36);\n\nSHOW CREATE TABLE opportunity_contacts;\nSELECT * FROM opportunity_contacts WHERE crm_provider_id = '111751';\n\n# $slug = 'HUBSPOT_WEBHOOK_SYNC';\n# $team = Jiminny\\Models\\Team::find(2);\n# $feature = Feature::query()->where('slug', $slug)->first();\n# TeamFeature::query()->create(['feature_id' => $feature->getId(),'team_id' => $team->getId()]);\n\n# hubspot_webhook_metrics\n\nselect * from opportunities where team_id = 2 and crm_provider_id IN ('374720564','14527423589','49908861993','50435771779'); # 1365\nSELECT * FROM opportunity_contacts WHERE opportunity_id = '414';\nSELECT * FROM opportunity_contacts WHERE crm_provider_id = '131501';\nselect * from contacts where id in (414, 464);\n\nselect * from activities where crm_configuration_id = 2;\n\nselect settings from crm_configurations where id = 11;\n\nselect * from teams; # 1, 2\nselect * from users;\nselect * from crm_configurations where id = 39;\nselect * from team_features where team_id = 2;\nselect * from features;\n# SELECT * FROM opportunities WHERE crm_configuration_id = 2\n# order by id desc;\n# and crm_provider_id = '49908861993';\n\n\nselect * from activity_providers where id IN (443, 202, 203, 227);\n\nselect * from activity_imports where id = 795889;\n\nselect c.id, c.provider, c.settings, t.* from teams t join crm_configurations c on t.id = c.team_id\nwhere c.provider = 'hubspot';\n\nselect * from crm_configurations crm JOIN teams t on crm.team_id = t.id\nwhere provider = 'hubspot';\nSELECT * FROM teams WHERE id = 31;\nSELECT * FROM users WHERE id = 257;\nSELECT * FROM opportunities WHERE team_id = 2;\n\nselect * from opportunity_contacts where opportunity_id = 5124;\nselect * from contacts where id IN (3850,3853,3851,4073,4140,4155,4480,4530,4623,5986,513,687,1806,1523,3613)\n\nselect * from activities where crm_configuration_id = 13;\n\nSELECT * FROM activities WHERE uuid_to_bin('826619ce-ec8e-4e59-8467-a01f5f6ad71e') = uuid; # 418141\n\n\nselect id, team_id, crm_provider_id from crm_configurations where provider = 'hubspot' and crm_provider_id IS NOT NULL;\nSELECT * FROM accounts WHERE team_id = 2 and crm_provider_id = '1212213464' order by id desc;\nSELECT * FROM contacts WHERE team_id = 2 and account_id = 5189 order by id desc;\nSELECT * FROM contacts WHERE team_id = 2 order by id desc;\nselect * from opportunity_contacts where contact_id = 6223;\nSELECT * FROM opportunities WHERE team_id = 2 and account_id = 5189 order by id desc;\n\nselect * from crm_profiles where crm_configuration_id = 2;\n\nselect * from activities where account_id = 46;","depth":4,"on_screen":true,"value":"# **************************** HS **************************************\n\nselect * from teams where id = 2; # 2\nselect * from features; # 2\nselect * from team_features where team_id = 2; # 2\nselect * from crm_configurations where id = 2; # 2\nselect * from users where team_id = 2; #\nselect * from playbooks where team_id = 2; # event 38\nselect * from playbook_categories where playbook_id = 38; #\n\nSELECT * FROM activities WHERE crm_configuration_id = 2 and crm_provider_id is not null order by id desc;\nhttps://app.hubspot.com/contacts/4392066/deal/16964514951/?engagement=96069102624\n https://app.staging.jiminny.com/playback/d5df34dc-bd66-4ff5-a7b3-8d3be30322a0\n\nSELECT * FROM activities WHERE uuid_to_bin('04fdcd0d-818f-4c53-92dc-6f18bc753ffd') = uuid;\n# 609126 softphone tr. 11241\n\nSELECT * FROM activities WHERE uuid_to_bin('6521bfcd-5a30-46e5-9f74-5440fd48befd') = uuid;\n# 608874 conference tr. 11226 crmId: 103422236596\n\nselect * from ai_prompts where transcription_id IN (11241, 11226);\nselect * from activity_summary_logs where activity_id = 608874;\n\nselect * from sidekick_settings;\nselect * from default_activity_types;\n\nselect * from crm_field_data where activity_id = 1223;\n\nselect * from crm_layouts where crm_configuration_id = 2;\nSELECT * FROM crm_layout_entities WHERE crm_layout_id IN (554);\nselect * from crm_fields where crm_configuration_id = 11 and object_type = 'event';\nSELECT * FROM crm_field_values WHERE crm_field_id IN (1455,1450);\n\nSELECT * FROM crm_field_data WHERE crm_layout_entity_id = 971;\nSELECT * FROM crm_field_data WHERE crm_layout_entity_id IN (6494,6495,6496,6497,6498,6499);\n\nSELECT\n CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,\n u.email,\n sa.*,\n t.owner_id FROM social_accounts sa\nJOIN users u\n on u.id = sa.sociable_id\nJOIN teams t on t.id = u.team_id\nWHERE u.team_id = 2 and sa.provider = 'hubspot';\n\nselect * from social_accounts where id = 1499;\n\nselect * from opportunities where team_id = 2\nand crm_provider_id IN ('51317301383');\n\nselect * from contacts where id = 85;\n\nselect * from opportunities where team_id = 2 order by id desc;\nselect * from opportunities where team_id = 2 and crm_provider_id = '51317301383'; # 5112\nselect * from opportunities where team_id = 2 and crm_provider_id = '55976759904'; # 5112\nselect * from opportunity_contacts where opportunity_id = 5117;\nselect * from crm_field_data where object_id = 1365;\nSELECT * FROM crm_fields WHERE id IN (1405, 1407, 1972, 2128);\n\nselect * from features;\nselect * from team_features where team_id IN (1);\nselect * from team_features where feature_id IN (36);\n\nSHOW CREATE TABLE opportunity_contacts;\nSELECT * FROM opportunity_contacts WHERE crm_provider_id = '111751';\n\n# $slug = 'HUBSPOT_WEBHOOK_SYNC';\n# $team = Jiminny\\Models\\Team::find(2);\n# $feature = Feature::query()->where('slug', $slug)->first();\n# TeamFeature::query()->create(['feature_id' => $feature->getId(),'team_id' => $team->getId()]);\n\n# hubspot_webhook_metrics\n\nselect * from opportunities where team_id = 2 and crm_provider_id IN ('374720564','14527423589','49908861993','50435771779'); # 1365\nSELECT * FROM opportunity_contacts WHERE opportunity_id = '414';\nSELECT * FROM opportunity_contacts WHERE crm_provider_id = '131501';\nselect * from contacts where id in (414, 464);\n\nselect * from activities where crm_configuration_id = 2;\n\nselect settings from crm_configurations where id = 11;\n\nselect * from teams; # 1, 2\nselect * from users;\nselect * from crm_configurations where id = 39;\nselect * from team_features where team_id = 2;\nselect * from features;\n# SELECT * FROM opportunities WHERE crm_configuration_id = 2\n# order by id desc;\n# and crm_provider_id = '49908861993';\n\n\nselect * from activity_providers where id IN (443, 202, 203, 227);\n\nselect * from activity_imports where id = 795889;\n\nselect c.id, c.provider, c.settings, t.* from teams t join crm_configurations c on t.id = c.team_id\nwhere c.provider = 'hubspot';\n\nselect * from crm_configurations crm JOIN teams t on crm.team_id = t.id\nwhere provider = 'hubspot';\nSELECT * FROM teams WHERE id = 31;\nSELECT * FROM users WHERE id = 257;\nSELECT * FROM opportunities WHERE team_id = 2;\n\nselect * from opportunity_contacts where opportunity_id = 5124;\nselect * from contacts where id IN (3850,3853,3851,4073,4140,4155,4480,4530,4623,5986,513,687,1806,1523,3613)\n\nselect * from activities where crm_configuration_id = 13;\n\nSELECT * FROM activities WHERE uuid_to_bin('826619ce-ec8e-4e59-8467-a01f5f6ad71e') = uuid; # 418141\n\n\nselect id, team_id, crm_provider_id from crm_configurations where provider = 'hubspot' and crm_provider_id IS NOT NULL;\nSELECT * FROM accounts WHERE team_id = 2 and crm_provider_id = '1212213464' order by id desc;\nSELECT * FROM contacts WHERE team_id = 2 and account_id = 5189 order by id desc;\nSELECT * FROM contacts WHERE team_id = 2 order by id desc;\nselect * from opportunity_contacts where contact_id = 6223;\nSELECT * FROM opportunities WHERE team_id = 2 and account_id = 5189 order by id desc;\n\nselect * from crm_profiles where crm_configuration_id = 2;\n\nselect * from activities where account_id = 46;","role_description":"text entry area","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Sync Changes","depth":4,"bounds":{"left":0.27027926,"top":1.0,"width":0.008643617,"height":0.0},"on_screen":false,"role_description":"button","is_enabled":false,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Hide This Notification","depth":4,"bounds":{"left":0.27027926,"top":1.0,"width":0.008643617,"height":0.0},"on_screen":false,"role_description":"button","is_enabled":false,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Code 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Error","depth":4,"bounds":{"left":0.27027926,"top":1.0,"width":0.00731383,"height":0.0},"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Next Highlighted Error","depth":4,"bounds":{"left":0.27027926,"top":1.0,"width":0.006981383,"height":0.0},"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXTextArea","text":"<?php\n\ndeclare(strict_types=1);\n\nnamespace Jiminny\\Services\\Crm\\Hubspot;\n\nuse HubSpot\\Client\\Crm\\Deals\\ApiException as DealApiException;\nuse HubSpot\\Client\\Crm\\Contacts\\ApiException as ContactApiException;\nuse HubSpot\\Client\\Crm\\Companies\\ApiException as CompanyApiException;\nuse HubSpot\\Client\\Crm\\Contacts\\Model\\SimplePublicObjectWithAssociations as ContactsWithAssociations;\nuse HubSpot\\Client\\Crm\\Companies\\Model\\SimplePublicObjectWithAssociations as CompaniesWithAssociations;\nuse HubSpot\\Client\\Crm\\Deals\\Model\\SimplePublicObjectWithAssociations as DealWithAssociations;\nuse HubSpot\\Client\\Crm\\Objects\\Model\\SimplePublicObjectInput;\nuse HubSpot\\Client\\Crm\\Objects\\Model\\SimplePublicObjectWithAssociations as ObjectWithAssociations;\nuse HubSpot\\Client\\Crm\\Pipelines\\Model\\Error;\nuse HubSpot\\Client\\Crm\\Pipelines\\Model\\PipelineStage;\nuse HubSpot\\Client\\Crm\\Properties\\Model\\Property;\nuse HubSpot\\Discovery\\Discovery;\nuse Jiminny\\Component\\Utility\\Service\\ProviderRateLimiter;\nuse Jiminny\\Exceptions\\CrmException;\nuse Jiminny\\Exceptions\\RateLimitException;\nuse Jiminny\\Exceptions\\SocialAccountTokenInvalidException;\nuse Jiminny\\Jobs\\Crm\\NoteObject;\nuse Jiminny\\Models\\Crm\\Field;\nuse Jiminny\\Services\\Crm\\BaseClient;\nuse Jiminny\\Services\\Crm\\Hubspot\\DTO\\Response\\Owner;\nuse Jiminny\\Services\\SocialAccountService;\nuse SevenShores\\Hubspot\\Exceptions\\BadRequest;\nuse SevenShores\\Hubspot\\Exceptions\\HubspotException;\nuse SevenShores\\Hubspot\\Factory;\nuse SevenShores\\Hubspot\\Http\\Response;\nuse Jiminny\\Services\\Crm\\Hubspot\\Pagination\\HubspotPaginationService;\nuse Throwable;\n\n/**\n * @phpstan-type CrmFieldOption array{id:string, label:string, value?:string}\n */\nclass Client extends BaseClient implements HubspotClientInterface\n{\n public const string MIN_API_VERSION = '2';\n\n public const string BASE_URL = 'https://api.hubapi.com';\n\n public const int ASSOCIATIONS_BATCH_SIZE_LIMIT = 1000;\n\n private HubspotPaginationService $paginationService;\n private HubspotTokenManager $tokenManager;\n private ProviderRateLimiter $rateLimiter;\n\n public function __construct(\n SocialAccountService $socialAccountService,\n HubspotPaginationService $paginationService,\n HubspotTokenManager $tokenManager,\n ProviderRateLimiter $rateLimiter,\n ) {\n parent::__construct($socialAccountService);\n $this->paginationService = $paginationService;\n $this->tokenManager = $tokenManager;\n $this->rateLimiter = $rateLimiter;\n\n $this->setBaseUrl(self::BASE_URL);\n $this->setVersion(self::MIN_API_VERSION);\n }\n\n /**\n * Single entry point for every HubSpot API call. Enforces the per-portal\n * rate limit configured in the rate_limits table (morphed to the current\n * Configuration) and reacts to a real 429 from HubSpot by translating it\n * into a RateLimitException carrying Retry-After.\n *\n * Wrap any outbound HubSpot call (SDK or raw HTTP) like:\n *\n * $this->executeRequest(fn () => $this->getNewInstance()->crm()->...);\n *\n * @template T\n * @param callable(): T $apiCall\n * @return T\n *\n * @throws RateLimitException\n */\n private function executeRequest(callable $apiCall)\n {\n if (! $this->rateLimiter->canMakeRequest($this->config)) {\n $retryAfter = $this->rateLimiter->requestAvailableIn($this->config);\n\n $this->log->warning('[Hubspot] Rate limit exceeded, deferring request', [\n 'team_id' => $this->config->team_id,\n 'config_id' => $this->config->getId(),\n 'retry_after' => $retryAfter,\n ]);\n\n throw new RateLimitException(\n 'Hubspot rate limit reached for configuration ' . $this->config->getId(),\n $retryAfter,\n );\n }\n\n $this->rateLimiter->incrementRequestCount($this->config);\n\n try {\n return $apiCall();\n } catch (Throwable $e) {\n if ($this->isHubspotRateLimit($e)) {\n $retryAfter = $this->parseRetryAfter($e);\n\n $this->log->warning('[Hubspot] Received 429 from API', [\n 'team_id' => $this->config->team_id,\n 'config_id' => $this->config->getId(),\n 'retry_after' => $retryAfter,\n 'reason' => $e->getMessage(),\n ]);\n\n throw new RateLimitException('Hubspot returned 429', $retryAfter, $e);\n }\n\n throw $e;\n }\n }\n\n private function isHubspotRateLimit(Throwable $e): bool\n {\n return method_exists($e, 'getCode') && (int) $e->getCode() === 429;\n }\n\n private function parseRetryAfter(Throwable $e): int\n {\n if (method_exists($e, 'getResponseHeaders')) {\n $headers = $e->getResponseHeaders() ?: [];\n $value = $headers['Retry-After'] ?? $headers['retry-after'] ?? null;\n if (is_array($value)) {\n $value = $value[0] ?? null;\n }\n if (is_numeric($value)) {\n return (int) $value;\n }\n }\n\n return 10;\n }\n\n public function getMinimumApiVersion(): string\n {\n return self::MIN_API_VERSION;\n }\n\n public function getInstance(): Factory\n {\n return new Factory([\n 'key' => $this->accessToken,\n 'oauth2' => true,\n 'base_url' => $this->baseUrl,\n ]);\n }\n\n public function getNewInstance(): Discovery\n {\n return \\HubSpot\\Factory::createWithAccessToken($this->accessToken);\n }\n\n /**\n * Secondly and daily limits for Hubspot API\n *\n * Product Tier: Free & Starter | Professional & Enterprise | API add-on (any tier)\n * Burst: 100/10 seconds | 150/10 seconds | 200/10 seconds\n * Daily: 250,000 | 500,000 | 1,000,000\n *\n * Official documentation states: The search endpoints are rate limited to five requests per second.\n * Since with 5 RPS were still hitting secondly rate limits we lowered it to 4\n */\n public function getPaginatedData(array $payload, string $type, int $offset = 0): array\n {\n $total = 0;\n $lastId = null;\n $rows = [];\n foreach ($this->getPaginatedDataGenerator($payload, $type, $offset, $total, $lastId) as $row) {\n $rows[] = $row;\n }\n\n return ['results' => $rows, 'total' => $total, 'last_record' => $lastId];\n }\n\n /**\n * @throws HubspotException\n * @throws SocialAccountTokenInvalidException\n * @throws BadRequest\n */\n public function getPaginatedDataGenerator(\n array $payload,\n string $type,\n int $offset = 0,\n int &$total = 0,\n ?string &$lastRecordId = null\n ): \\Generator {\n return $this->paginationService->getPaginatedDataGenerator(\n $this,\n $payload,\n $type,\n $offset,\n $total,\n $lastRecordId\n );\n }\n\n /**\n * @throws DealApiException\n * @throws CrmException\n */\n public function getOpportunityById(string $crmId, array $fields): array\n {\n try {\n $deal = $this->executeRequest(fn () => $this->getNewInstance()->crm()->deals()->basicApi()->getById(\n $crmId,\n implode(',', $fields),\n 'companies,contacts'\n ));\n } catch (DealApiException $e) {\n $this->log->info('[Hubspot] Failed to fetch opportunity', [\n 'crm_id' => $crmId,\n 'reason' => $e->getMessage(),\n ]);\n\n throw $e;\n }\n\n if (! $deal instanceof DealWithAssociations) {\n throw new CrmException('Deal not found');\n }\n\n return [\n 'id' => $deal->getId(),\n 'properties' => $deal->getProperties(),\n 'associations' => $deal->getAssociations(),\n ];\n }\n\n /**\n * Generic batch read method for HubSpot objects\n *\n * @param string $objectType The object type ('deals', 'companies', 'contacts')\n * @param array<string> $crmIds Array of HubSpot object IDs (max 100)\n * @param array<string> $fields Array of property names to fetch\n *\n * @return array<string, array> Array keyed by CRM ID with object data\n */\n private function batchReadObjects(string $objectType, array $crmIds, array $fields): array\n {\n if (empty($crmIds)) {\n return [];\n }\n\n $this->validateBatchSize($objectType, $crmIds);\n $this->ensureValidToken();\n\n try {\n $batchConfig = $this->createBatchConfiguration($objectType);\n $batchReadRequest = $this->prepareBatchRequest($batchConfig, $crmIds, $fields);\n $response = $batchConfig['api']->read($batchReadRequest);\n\n $this->validateApiResponse($response, $objectType);\n\n $results = $this->processApiResults($response);\n $this->logBatchResults($objectType, $crmIds, $results);\n\n return $results;\n } catch (\\Throwable $e) {\n $this->handleBatchError($e, $objectType, $crmIds);\n }\n }\n\n private function validateBatchSize(string $objectType, array $crmIds): void\n {\n if (count($crmIds) > 100) {\n throw new \\InvalidArgumentException(\"Batch size cannot exceed 100 {$objectType}\");\n }\n }\n\n private function createBatchConfiguration(string $objectType): array\n {\n $configurations = [\n 'deals' => [\n 'batchReadRequest' => new \\HubSpot\\Client\\Crm\\Deals\\Model\\BatchReadInputSimplePublicObjectId(),\n 'inputClass' => \\HubSpot\\Client\\Crm\\Deals\\Model\\SimplePublicObjectId::class,\n 'api' => $this->getNewInstance()->crm()->deals()->batchApi(),\n ],\n 'companies' => [\n 'batchReadRequest' => new \\HubSpot\\Client\\Crm\\Companies\\Model\\BatchReadInputSimplePublicObjectId(),\n 'inputClass' => \\HubSpot\\Client\\Crm\\Companies\\Model\\SimplePublicObjectId::class,\n 'api' => $this->getNewInstance()->crm()->companies()->batchApi(),\n ],\n 'contacts' => [\n 'batchReadRequest' => new \\HubSpot\\Client\\Crm\\Contacts\\Model\\BatchReadInputSimplePublicObjectId(),\n 'inputClass' => \\HubSpot\\Client\\Crm\\Contacts\\Model\\SimplePublicObjectId::class,\n 'api' => $this->getNewInstance()->crm()->contacts()->batchApi(),\n ],\n ];\n\n if (! isset($configurations[$objectType])) {\n throw new \\InvalidArgumentException(\"Unsupported object type: {$objectType}\");\n }\n\n return $configurations[$objectType];\n }\n\n private function prepareBatchRequest(array $batchConfig, array $crmIds, array $fields): object\n {\n $batchReadRequest = $batchConfig['batchReadRequest'];\n $inputClass = $batchConfig['inputClass'];\n\n $inputs = array_map(function ($crmId) use ($inputClass) {\n $input = new $inputClass();\n $input->setId($crmId);\n\n return $input;\n }, $crmIds);\n\n $batchReadRequest->setInputs($inputs);\n $batchReadRequest->setProperties($fields);\n\n return $batchReadRequest;\n }\n\n private function validateApiResponse($response, string $objectType): void\n {\n if (! $response) {\n throw new CrmException(\"HubSpot API returned null response for {$objectType} batch read\");\n }\n }\n\n private function processApiResults($response): array\n {\n $results = [];\n $responseResults = $response->getResults();\n\n if ($responseResults) {\n foreach ($responseResults as $object) {\n if ($object && $object->getId()) {\n $results[$object->getId()] = [\n 'id' => $object->getId(),\n 'properties' => $object->getProperties() ?: [],\n ];\n }\n }\n }\n\n return $results;\n }\n\n private function logBatchResults(string $objectType, array $crmIds, array $results): void\n {\n $this->log->info(\"[HubSpot] Batch fetched {$objectType}\", [\n 'requested_count' => count($crmIds),\n 'returned_count' => count($results),\n 'crm_ids' => $crmIds,\n ]);\n }\n\n private function handleBatchError(\\Throwable $e, string $objectType, array $crmIds): void\n {\n $errorMessage = $e->getMessage() ?: 'Unknown error';\n $errorTrace = $e->getTraceAsString() ?: 'No trace available';\n\n $this->log->error(\"[HubSpot] Failed to batch fetch {$objectType}\", [\n 'crm_ids' => $crmIds,\n 'error' => $errorMessage,\n 'trace' => $errorTrace,\n ]);\n\n throw new CrmException(\"Failed to batch fetch {$objectType}: \" . $errorMessage);\n }\n\n /**\n * Batch read multiple opportunities by their CRM IDs\n *\n * @param array<string> $crmIds Array of HubSpot deal IDs (max 100)\n * @param array<string> $fields Array of property names to fetch\n *\n * @return array<string, array> Array keyed by CRM ID with opportunity data\n */\n public function getOpportunitiesByIds(array $crmIds, array $fields): array\n {\n return $this->batchReadObjects('deals', $crmIds, $fields);\n }\n\n /**\n * Batch read multiple companies by their CRM IDs\n *\n * @param array<string> $crmIds Array of HubSpot company IDs (max 100)\n * @param array<string> $fields Array of property names to fetch\n *\n * @return array<string, array> Array keyed by CRM ID with company data\n */\n public function getCompaniesByIds(array $crmIds, array $fields): array\n {\n return $this->batchReadObjects('companies', $crmIds, $fields);\n }\n\n /**\n * Batch read multiple contacts by their CRM IDs\n *\n * @param array<string> $crmIds Array of HubSpot contact IDs (max 100)\n * @param array<string> $fields Array of property names to fetch\n *\n * @return array<string, array> Array keyed by CRM ID with contact data\n */\n public function getContactsByIds(array $crmIds, array $fields): array\n {\n return $this->batchReadObjects('contacts', $crmIds, $fields);\n }\n\n /**\n * @throws CompanyApiException\n * @throws CrmException\n */\n public function getAccountById(string $crmId, array $fields): array\n {\n try {\n $company = $this->getNewInstance()->crm()->companies()->basicApi()->getById(\n $crmId,\n implode(',', $fields),\n );\n } catch (CompanyApiException $e) {\n $this->log->info('[Hubspot] Failed to fetch account', [\n 'crm_id' => $crmId,\n 'reason' => $e->getMessage(),\n ]);\n\n throw $e;\n }\n\n if (! $company instanceof CompaniesWithAssociations) {\n throw new CrmException('Account not found');\n }\n\n return [\n 'id' => $company->getId(),\n 'properties' => $company->getProperties(),\n ];\n }\n\n /**\n * @throws ContactApiException\n * @throws CrmException\n */\n public function getContactById(string $crmId, array $fields): array\n {\n try {\n $contact = $this->getNewInstance()->crm()->contacts()->basicApi()->getById(\n $crmId,\n implode(',', $fields)\n );\n } catch (ContactApiException $e) {\n $this->log->info('[Hubspot] Failed to fetch contact', [\n 'crm_id' => $crmId,\n 'reason' => $e->getMessage(),\n ]);\n\n throw $e;\n }\n\n if (! $contact instanceof ContactsWithAssociations) {\n throw new CrmException('Contact not found');\n }\n\n return [\n 'id' => $contact->getId(),\n 'properties' => $contact->getProperties(),\n ];\n }\n\n /**\n * This is email search request that Hubspot offers as GET (more generous quota)\n */\n public function getContactByEmail(string $email, array $fields = []): array\n {\n try {\n $contact = $this->getNewInstance()->crm()->contacts()->basicApi()->getById(\n $email,\n implode(',', $fields),\n null,\n false,\n 'email'\n );\n\n return [\n 'id' => $contact->getId(),\n 'properties' => $contact->getProperties(),\n ];\n } catch (ContactApiException $e) {\n $this->log->info('[Hubspot] Failed to fetch contact', [\n 'email' => $email,\n 'reason' => $e->getMessage(),\n ]);\n\n return [];\n }\n }\n\n /**\n * @throws CrmException\n */\n public function fetchProperty(string $objectType, string $propertyId): Property\n {\n $result = $this->getNewInstance()->crm()->properties()->coreApi()->getByName($objectType, $propertyId);\n\n if (! $result instanceof Property) {\n $this->log->error('[Hubspot] Failed to fetch property', [\n 'object_type' => $objectType,\n 'property_id' => $propertyId,\n 'reason' => $result->getMessage(),\n ]);\n\n throw new CrmException('Failed to fetch property');\n }\n\n return $result;\n }\n\n /**\n * @return array<CrmFieldOption>\n */\n public function fetchPropertyOptions(string $objectType, string $propertyId): array\n {\n /** @var array<CrmFieldOption> */\n return $this->fetchProperty($objectType, $propertyId)->getOptions();\n }\n\n /**\n * @return array<array{id:string, label:string, deleted:bool}>\n */\n public function fetchCallDispositions(): array\n {\n /** @var Response $response */\n $response = $this->getInstance()->engagements()->getCallDispositions();\n\n /**\n * @var array<array{\n * id:string,\n * label:string,\n * deleted: bool\n * }>\n */\n return $response->toArray();\n }\n\n /**\n * @return array<CrmFieldOption>\n */\n public function fetchOpportunityPipelineStages(): array\n {\n $stages = [];\n $apiResponse = $this->getNewInstance()->crm()->pipelines()->pipelinesApi()->getAll('deals');\n\n if ($apiResponse instanceof Error) {\n $this->log->error('[Hubspot] Failed to fetch opportunity pipelines', [\n 'reason' => $apiResponse->getMessage(),\n ]);\n\n return [];\n }\n\n foreach ($apiResponse->getResults() as $pipeline) {\n $pipelineStages = array_map(\n static function (PipelineStage $stage) {\n return [\n 'id' => $stage->getId(),\n 'label' => $stage->getLabel(),\n ];\n },\n $pipeline->getStages()\n );\n\n $stages = array_merge($stages, $pipelineStages);\n }\n\n return $stages;\n }\n\n public function fetchOpportunityPipelines(): array\n {\n $pipelines = [];\n\n try {\n $apiResponse = $this->makeRequest('/crm/v3/pipelines/deals');\n } catch (\\Exception $e) {\n $this->log->info('[Hubspot] Failed to fetch opportunity pipelines', [\n 'reason' => $e->getMessage(),\n ]);\n\n return [];\n }\n\n $response = $apiResponse->toArray();\n\n foreach ($response['results'] as $pipeline) {\n $pipelines[] = [\n 'id' => $pipeline['id'],\n 'label' => $pipeline['label'],\n ];\n }\n\n return $pipelines;\n }\n\n /**\n * @return array<CrmFieldOption>\n */\n public function fetchMeetingOutcomeFieldOptions(Field $field): array\n {\n return $field->getCrmProviderId() === 'meetingOutcome'\n ? $this->fetchMeetingOutcomeTypes()\n : $this->fetchCallActivityTypes();\n }\n\n public function fetchMeetingOutcomeTypes(): array\n {\n return $this->extractMeetingTypeOptions(\n 'https://api.hubapi.com/crm/v3/properties/meeting/hs_meeting_outcome'\n );\n }\n\n public function fetchCallActivityTypes(): array\n {\n return $this->extractMeetingTypeOptions(\n 'https://api.hubapi.com/crm/v3/properties/call/hs_activity_type'\n );\n }\n\n private function extractMeetingTypeOptions(string $endpoint): array\n {\n /** @var Response $response */\n $response = $this->getInstance()\n ->getClient()\n ->request('GET', $endpoint);\n\n /**\n * @var array<array{\n * value: string,\n * label: string,\n * displayOrder: int\n * }> $optionData\n */\n $optionData = $response->toArray()['options'] ?? [];\n\n $options = [];\n foreach ($optionData as $item) {\n $options[] = [\n 'id' => $item['value'],\n 'value' => $item['value'],\n 'label' => $item['label'],\n 'display_order' => $item['displayOrder'],\n ];\n }\n\n return $options;\n }\n\n /**\n * @return array<CrmFieldOption>\n */\n public function fetchDispositionFieldOptions(): array\n {\n $options = [];\n\n $dispositions = $this->fetchCallDispositions();\n\n foreach ($dispositions as $disposition) {\n if ($disposition['deleted'] !== false) {\n continue;\n }\n\n $option['value'] = $disposition['id'];\n $option['id'] = $disposition['id'];\n $option['label'] = $disposition['label'];\n\n $options[] = $option;\n }\n\n return $options;\n }\n\n /**\n * @return array<CrmFieldOption>\n */\n public function fetchOpportunityFieldOptions(Field $field): array\n {\n if ($field->isStageField()) {\n return $this->fetchOpportunityPipelineStages();\n }\n\n if ($field->isPipelineField()) {\n return $this->fetchOpportunityPipelines();\n }\n\n return $this->fetchPropertyOptions('deals', $field->getCrmProviderId());\n }\n\n /**\n * @throws BadRequest\n * @throws HubspotException\n */\n public function makeRequest(string $endpoint, $method = 'GET', $payload = [], ?string $queryString = null)\n {\n $endpoint = self::BASE_URL . $endpoint;\n\n if ($method === 'GET') {\n return $this->getInstance()->getClient()?->request(\n method: $method,\n endpoint: $endpoint,\n query_string: $queryString\n );\n } else {\n return $this->getInstance()->getClient()->request($method, $endpoint, [\n 'json' => ($payload),\n ]);\n }\n }\n\n /**\n * @throws BadRequest\n * @throws HubspotException\n */\n public function createMeeting(array $payload): Response\n {\n $endpoint = '/crm/v3/objects/meetings';\n\n return $this->makeRequest($endpoint, 'POST', $payload);\n }\n\n /**\n * @throws BadRequest\n * @throws HubspotException\n */\n public function updateMeeting(string $meetingId, array $payload): Response\n {\n $endpoint = '/crm/v3/objects/meetings/' . $meetingId;\n\n return $this->makeRequest($endpoint, 'PATCH', $payload);\n }\n\n /**\n * @throws \\Exception\n */\n public function createNote(\n string $body,\n string $ownerId,\n int $timestamp,\n string $objectId,\n NoteObject $noteObject\n ): ?string {\n try {\n $noteInput = new SimplePublicObjectInput([\n 'properties' => [\n 'hs_note_body' => $body,\n 'hubspot_owner_id' => $ownerId,\n 'hs_timestamp' => $timestamp,\n ],\n ]);\n\n // Create note\n $note = $this->getNewInstance()->crm()->objects()->basicApi()->create('note', $noteInput);\n\n $this->getNewInstance()->crm()->objects()->associationsApi()->create(\n 'note',\n $note->getId(),\n $this->getNoteObject($noteObject),\n $objectId,\n $this->getNoteAssociationType($noteObject),\n );\n\n return $note->getId();\n } catch (\\Exception $e) {\n $this->log->error('[Hubspot] Failed to create note', [\n 'objectId' => $objectId,\n 'noteObject' => $noteObject->getObjectType(),\n 'reason' => $e->getMessage(),\n ]);\n\n \\Sentry::captureException($e);\n }\n\n return null;\n }\n\n public function updateEngagement(string $objectId, array $engagement, array $metadata): void\n {\n $this->getInstance()->engagements()->update($objectId, $engagement, $metadata);\n }\n\n public function getEngagementData(string $engagementId): array\n {\n $engagement = $this->getInstance()->engagements()->get($engagementId);\n\n return $engagement->toArray();\n }\n\n public function createEngagement(array $engagement, array $associations, array $metadata): Response\n {\n return $this->getInstance()\n ->engagements()\n ->create($engagement, $associations, $metadata);\n }\n\n public function isUnauthorizedException(\\Exception $e): bool\n {\n // Check for specific HubSpot API exception types first\n if ($e instanceof BadRequest) {\n // BadRequest can contain 401 status codes\n return $e->getCode() === 401;\n }\n\n // Check for HTTP client exceptions with status codes\n if ($e instanceof \\GuzzleHttp\\Exception\\RequestException && $e->hasResponse()) {\n $response = $e->getResponse();\n if ($response !== null) {\n return $response->getStatusCode() === 401;\n }\n }\n\n // Check for Guzzle HTTP exceptions\n if ($e instanceof \\GuzzleHttp\\Exception\\ClientException) {\n return $e->getCode() === 401;\n }\n\n // Fallback to string matching as last resort, but be more specific\n $message = strtolower($e->getMessage());\n\n return str_contains($message, '401 unauthorized') ||\n str_contains($message, 'http 401') ||\n str_contains($message, 'status code 401') ||\n (preg_match('/\\b401\\b/', $message) && str_contains($message, 'unauthorized'));\n }\n\n /**\n * Validates and refreshes the access token if needed before API requests.\n * This ensures long-running processes don't fail due to token expiration.\n *\n * @throws SocialAccountTokenInvalidException\n */\n public function ensureValidToken(): void\n {\n if ($this->oauthAccount === null) {\n return;\n }\n\n $newToken = $this->tokenManager->ensureValidToken($this->oauthAccount);\n if ($newToken !== null) {\n $this->accessToken = $newToken;\n }\n }\n\n public function getConfig()\n {\n return $this->config;\n }\n\n // returns only active (archived=false)\n public function getOwners(): array\n {\n return $this->getNewInstance()->crm()->owners()->getAll();\n }\n\n /**\n * @param bool $archived\n *\n * @return array<Owner>|[]\n */\n public function getOwnersArchived(bool $archived = true): array\n {\n $endpoint = '/crm/v3/owners';\n $queryParams = [\n 'archived' => $archived ? 'true' : 'false',\n ];\n $queryString = http_build_query($queryParams);\n\n $owners = [];\n\n try {\n $response = $this->makeRequest(endpoint: $endpoint, queryString: $queryString);\n $responseData = $response?->toArray();\n\n foreach ($responseData['results'] as $result) {\n try {\n $owners[] = Owner::create($result);\n } catch (Throwable $e) {\n $this->log->error('[HubSpot] Failed to process owner data', [\n 'result' => $result,\n 'error' => $e->getMessage(),\n ]);\n\n continue;\n }\n }\n } catch (Throwable $e) {\n $this->log->error('HubSpot] Failed to fetch owners', [\n 'archived' => $archived,\n 'error' => $e->getMessage(),\n ]);\n\n return [];\n }\n\n return $owners;\n }\n\n public function getMeeting(string $engagementId): ObjectWithAssociations\n {\n return $this->getNewInstance()->crm()->objects()->basicApi()\n ->getById('meeting', $engagementId, null, 'contact,company,deal');\n }\n\n public function deleteEngagement(string $engagementId): void\n {\n $this->getInstance()->engagements()->delete((int) $engagementId);\n }\n\n public function getAssociationsData(array $ids, string $fromObject, string $toObject): array\n {\n $associationData = [];\n $idChunks = array_chunk($ids, self::ASSOCIATIONS_BATCH_SIZE_LIMIT);\n\n foreach ($idChunks as $idChunk) {\n try {\n $batchInput = new \\HubSpot\\Client\\Crm\\Associations\\Model\\BatchInputPublicObjectId();\n $batchInput->setInputs(array_map(function ($id) {\n $publicObjectId = new \\HubSpot\\Client\\Crm\\Associations\\Model\\PublicObjectId();\n $publicObjectId->setId($id);\n\n return $publicObjectId;\n }, $idChunk));\n\n $associatedObjectsData = $this\n ->getNewInstance()\n ->crm()\n ->associations()\n ->batchApi()\n ->read($fromObject, $toObject, $batchInput);\n\n if ($associatedObjectsData instanceof \\HubSpot\\Client\\Crm\\Associations\\Model\\BatchResponsePublicAssociationMulti) {\n foreach ($associatedObjectsData->getResults() as $association) {\n $from = $association->getFrom()->getId();\n $toAssociations = $association->getTo();\n\n if (! empty($toAssociations)) {\n $associationData[$from] = array_map(function ($item) {\n return $item->getId();\n }, $toAssociations);\n }\n }\n }\n } catch (\\Exception $e) {\n $this->log->error('[Hubspot] Failed to fetch associations', [\n 'from_object' => $fromObject,\n 'to_object' => $toObject,\n 'reason' => $e->getMessage(),\n ]);\n }\n }\n\n return $associationData;\n }\n\n /**\n * @throws \\Exception\n */\n private function getNoteAssociationType(NoteObject $noteObject): string\n {\n return match($noteObject) {\n NoteObject::Opportunity => 'note_to_deal',\n NoteObject::Lead, NoteObject::Contact => 'note_to_contact', // or 'note_to_lead' if your portal supports it\n NoteObject::Account => 'note_to_company',\n NoteObject::Call, NoteObject::Event => throw new \\Exception('Not supported'),\n };\n }\n\n /**\n * @throws \\Exception\n */\n private function getNoteObject(NoteObject $noteObject): string\n {\n return match($noteObject) {\n NoteObject::Opportunity => 'deal',\n NoteObject::Lead, NoteObject::Contact => 'contact',\n NoteObject::Account => 'company',\n NoteObject::Call, NoteObject::Event => throw new \\Exception('Not supported'),\n };\n }\n\n public function addAssociations(string $objectType, string $associationType, array $payload): Response\n {\n $endpoint = \"/crm/v4/associations/$objectType/$associationType/batch/create\";\n\n return $this->makeRequest($endpoint, 'POST', $payload);\n }\n\n public function removeAssociations(string $objectType, string $associationType, array $payload): Response\n {\n $endpoint = \"/crm/v4/associations/$objectType/$associationType/batch/archive\";\n\n return $this->makeRequest($endpoint, 'POST', $payload);\n }\n}","depth":4,"on_screen":true,"value":"<?php\n\ndeclare(strict_types=1);\n\nnamespace Jiminny\\Services\\Crm\\Hubspot;\n\nuse HubSpot\\Client\\Crm\\Deals\\ApiException as DealApiException;\nuse HubSpot\\Client\\Crm\\Contacts\\ApiException as ContactApiException;\nuse HubSpot\\Client\\Crm\\Companies\\ApiException as CompanyApiException;\nuse HubSpot\\Client\\Crm\\Contacts\\Model\\SimplePublicObjectWithAssociations as ContactsWithAssociations;\nuse HubSpot\\Client\\Crm\\Companies\\Model\\SimplePublicObjectWithAssociations as CompaniesWithAssociations;\nuse HubSpot\\Client\\Crm\\Deals\\Model\\SimplePublicObjectWithAssociations as DealWithAssociations;\nuse HubSpot\\Client\\Crm\\Objects\\Model\\SimplePublicObjectInput;\nuse HubSpot\\Client\\Crm\\Objects\\Model\\SimplePublicObjectWithAssociations as ObjectWithAssociations;\nuse HubSpot\\Client\\Crm\\Pipelines\\Model\\Error;\nuse HubSpot\\Client\\Crm\\Pipelines\\Model\\PipelineStage;\nuse HubSpot\\Client\\Crm\\Properties\\Model\\Property;\nuse HubSpot\\Discovery\\Discovery;\nuse Jiminny\\Component\\Utility\\Service\\ProviderRateLimiter;\nuse Jiminny\\Exceptions\\CrmException;\nuse Jiminny\\Exceptions\\RateLimitException;\nuse Jiminny\\Exceptions\\SocialAccountTokenInvalidException;\nuse Jiminny\\Jobs\\Crm\\NoteObject;\nuse Jiminny\\Models\\Crm\\Field;\nuse Jiminny\\Services\\Crm\\BaseClient;\nuse Jiminny\\Services\\Crm\\Hubspot\\DTO\\Response\\Owner;\nuse Jiminny\\Services\\SocialAccountService;\nuse SevenShores\\Hubspot\\Exceptions\\BadRequest;\nuse SevenShores\\Hubspot\\Exceptions\\HubspotException;\nuse SevenShores\\Hubspot\\Factory;\nuse SevenShores\\Hubspot\\Http\\Response;\nuse Jiminny\\Services\\Crm\\Hubspot\\Pagination\\HubspotPaginationService;\nuse Throwable;\n\n/**\n * @phpstan-type CrmFieldOption array{id:string, label:string, value?:string}\n */\nclass Client extends BaseClient implements HubspotClientInterface\n{\n public const string MIN_API_VERSION = '2';\n\n public const string BASE_URL = 'https://api.hubapi.com';\n\n public const int ASSOCIATIONS_BATCH_SIZE_LIMIT = 1000;\n\n private HubspotPaginationService $paginationService;\n private HubspotTokenManager $tokenManager;\n private ProviderRateLimiter $rateLimiter;\n\n public function __construct(\n SocialAccountService $socialAccountService,\n HubspotPaginationService $paginationService,\n HubspotTokenManager $tokenManager,\n ProviderRateLimiter $rateLimiter,\n ) {\n parent::__construct($socialAccountService);\n $this->paginationService = $paginationService;\n $this->tokenManager = $tokenManager;\n $this->rateLimiter = $rateLimiter;\n\n $this->setBaseUrl(self::BASE_URL);\n $this->setVersion(self::MIN_API_VERSION);\n }\n\n /**\n * Single entry point for every HubSpot API call. Enforces the per-portal\n * rate limit configured in the rate_limits table (morphed to the current\n * Configuration) and reacts to a real 429 from HubSpot by translating it\n * into a RateLimitException carrying Retry-After.\n *\n * Wrap any outbound HubSpot call (SDK or raw HTTP) like:\n *\n * $this->executeRequest(fn () => $this->getNewInstance()->crm()->...);\n *\n * @template T\n * @param callable(): T $apiCall\n * @return T\n *\n * @throws RateLimitException\n */\n private function executeRequest(callable $apiCall)\n {\n if (! $this->rateLimiter->canMakeRequest($this->config)) {\n $retryAfter = $this->rateLimiter->requestAvailableIn($this->config);\n\n $this->log->warning('[Hubspot] Rate limit exceeded, deferring request', [\n 'team_id' => $this->config->team_id,\n 'config_id' => $this->config->getId(),\n 'retry_after' => $retryAfter,\n ]);\n\n throw new RateLimitException(\n 'Hubspot rate limit reached for configuration ' . $this->config->getId(),\n $retryAfter,\n );\n }\n\n $this->rateLimiter->incrementRequestCount($this->config);\n\n try {\n return $apiCall();\n } catch (Throwable $e) {\n if ($this->isHubspotRateLimit($e)) {\n $retryAfter = $this->parseRetryAfter($e);\n\n $this->log->warning('[Hubspot] Received 429 from API', [\n 'team_id' => $this->config->team_id,\n 'config_id' => $this->config->getId(),\n 'retry_after' => $retryAfter,\n 'reason' => $e->getMessage(),\n ]);\n\n throw new RateLimitException('Hubspot returned 429', $retryAfter, $e);\n }\n\n throw $e;\n }\n }\n\n private function isHubspotRateLimit(Throwable $e): bool\n {\n return method_exists($e, 'getCode') && (int) $e->getCode() === 429;\n }\n\n private function parseRetryAfter(Throwable $e): int\n {\n if (method_exists($e, 'getResponseHeaders')) {\n $headers = $e->getResponseHeaders() ?: [];\n $value = $headers['Retry-After'] ?? $headers['retry-after'] ?? null;\n if (is_array($value)) {\n $value = $value[0] ?? null;\n }\n if (is_numeric($value)) {\n return (int) $value;\n }\n }\n\n return 10;\n }\n\n public function getMinimumApiVersion(): string\n {\n return self::MIN_API_VERSION;\n }\n\n public function getInstance(): Factory\n {\n return new Factory([\n 'key' => $this->accessToken,\n 'oauth2' => true,\n 'base_url' => $this->baseUrl,\n ]);\n }\n\n public function getNewInstance(): Discovery\n {\n return \\HubSpot\\Factory::createWithAccessToken($this->accessToken);\n }\n\n /**\n * Secondly and daily limits for Hubspot API\n *\n * Product Tier: Free & Starter | Professional & Enterprise | API add-on (any tier)\n * Burst: 100/10 seconds | 150/10 seconds | 200/10 seconds\n * Daily: 250,000 | 500,000 | 1,000,000\n *\n * Official documentation states: The search endpoints are rate limited to five requests per second.\n * Since with 5 RPS were still hitting secondly rate limits we lowered it to 4\n */\n public function getPaginatedData(array $payload, string $type, int $offset = 0): array\n {\n $total = 0;\n $lastId = null;\n $rows = [];\n foreach ($this->getPaginatedDataGenerator($payload, $type, $offset, $total, $lastId) as $row) {\n $rows[] = $row;\n }\n\n return ['results' => $rows, 'total' => $total, 'last_record' => $lastId];\n }\n\n /**\n * @throws HubspotException\n * @throws SocialAccountTokenInvalidException\n * @throws BadRequest\n */\n public function getPaginatedDataGenerator(\n array $payload,\n string $type,\n int $offset = 0,\n int &$total = 0,\n ?string &$lastRecordId = null\n ): \\Generator {\n return $this->paginationService->getPaginatedDataGenerator(\n $this,\n $payload,\n $type,\n $offset,\n $total,\n $lastRecordId\n );\n }\n\n /**\n * @throws DealApiException\n * @throws CrmException\n */\n public function getOpportunityById(string $crmId, array $fields): array\n {\n try {\n $deal = $this->executeRequest(fn () => $this->getNewInstance()->crm()->deals()->basicApi()->getById(\n $crmId,\n implode(',', $fields),\n 'companies,contacts'\n ));\n } catch (DealApiException $e) {\n $this->log->info('[Hubspot] Failed to fetch opportunity', [\n 'crm_id' => $crmId,\n 'reason' => $e->getMessage(),\n ]);\n\n throw $e;\n }\n\n if (! $deal instanceof DealWithAssociations) {\n throw new CrmException('Deal not found');\n }\n\n return [\n 'id' => $deal->getId(),\n 'properties' => $deal->getProperties(),\n 'associations' => $deal->getAssociations(),\n ];\n }\n\n /**\n * Generic batch read method for HubSpot objects\n *\n * @param string $objectType The object type ('deals', 'companies', 'contacts')\n * @param array<string> $crmIds Array of HubSpot object IDs (max 100)\n * @param array<string> $fields Array of property names to fetch\n *\n * @return array<string, array> Array keyed by CRM ID with object data\n */\n private function batchReadObjects(string $objectType, array $crmIds, array $fields): array\n {\n if (empty($crmIds)) {\n return [];\n }\n\n $this->validateBatchSize($objectType, $crmIds);\n $this->ensureValidToken();\n\n try {\n $batchConfig = $this->createBatchConfiguration($objectType);\n $batchReadRequest = $this->prepareBatchRequest($batchConfig, $crmIds, $fields);\n $response = $batchConfig['api']->read($batchReadRequest);\n\n $this->validateApiResponse($response, $objectType);\n\n $results = $this->processApiResults($response);\n $this->logBatchResults($objectType, $crmIds, $results);\n\n return $results;\n } catch (\\Throwable $e) {\n $this->handleBatchError($e, $objectType, $crmIds);\n }\n }\n\n private function validateBatchSize(string $objectType, array $crmIds): void\n {\n if (count($crmIds) > 100) {\n throw new \\InvalidArgumentException(\"Batch size cannot exceed 100 {$objectType}\");\n }\n }\n\n private function createBatchConfiguration(string $objectType): array\n {\n $configurations = [\n 'deals' => [\n 'batchReadRequest' => new \\HubSpot\\Client\\Crm\\Deals\\Model\\BatchReadInputSimplePublicObjectId(),\n 'inputClass' => \\HubSpot\\Client\\Crm\\Deals\\Model\\SimplePublicObjectId::class,\n 'api' => $this->getNewInstance()->crm()->deals()->batchApi(),\n ],\n 'companies' => [\n 'batchReadRequest' => new \\HubSpot\\Client\\Crm\\Companies\\Model\\BatchReadInputSimplePublicObjectId(),\n 'inputClass' => \\HubSpot\\Client\\Crm\\Companies\\Model\\SimplePublicObjectId::class,\n 'api' => $this->getNewInstance()->crm()->companies()->batchApi(),\n ],\n 'contacts' => [\n 'batchReadRequest' => new \\HubSpot\\Client\\Crm\\Contacts\\Model\\BatchReadInputSimplePublicObjectId(),\n 'inputClass' => \\HubSpot\\Client\\Crm\\Contacts\\Model\\SimplePublicObjectId::class,\n 'api' => $this->getNewInstance()->crm()->contacts()->batchApi(),\n ],\n ];\n\n if (! isset($configurations[$objectType])) {\n throw new \\InvalidArgumentException(\"Unsupported object type: {$objectType}\");\n }\n\n return $configurations[$objectType];\n }\n\n private function prepareBatchRequest(array $batchConfig, array $crmIds, array $fields): object\n {\n $batchReadRequest = $batchConfig['batchReadRequest'];\n $inputClass = $batchConfig['inputClass'];\n\n $inputs = array_map(function ($crmId) use ($inputClass) {\n $input = new $inputClass();\n $input->setId($crmId);\n\n return $input;\n }, $crmIds);\n\n $batchReadRequest->setInputs($inputs);\n $batchReadRequest->setProperties($fields);\n\n return $batchReadRequest;\n }\n\n private function validateApiResponse($response, string $objectType): void\n {\n if (! $response) {\n throw new CrmException(\"HubSpot API returned null response for {$objectType} batch read\");\n }\n }\n\n private function processApiResults($response): array\n {\n $results = [];\n $responseResults = $response->getResults();\n\n if ($responseResults) {\n foreach ($responseResults as $object) {\n if ($object && $object->getId()) {\n $results[$object->getId()] = [\n 'id' => $object->getId(),\n 'properties' => $object->getProperties() ?: [],\n ];\n }\n }\n }\n\n return $results;\n }\n\n private function logBatchResults(string $objectType, array $crmIds, array $results): void\n {\n $this->log->info(\"[HubSpot] Batch fetched {$objectType}\", [\n 'requested_count' => count($crmIds),\n 'returned_count' => count($results),\n 'crm_ids' => $crmIds,\n ]);\n }\n\n private function handleBatchError(\\Throwable $e, string $objectType, array $crmIds): void\n {\n $errorMessage = $e->getMessage() ?: 'Unknown error';\n $errorTrace = $e->getTraceAsString() ?: 'No trace available';\n\n $this->log->error(\"[HubSpot] Failed to batch fetch {$objectType}\", [\n 'crm_ids' => $crmIds,\n 'error' => $errorMessage,\n 'trace' => $errorTrace,\n ]);\n\n throw new CrmException(\"Failed to batch fetch {$objectType}: \" . $errorMessage);\n }\n\n /**\n * Batch read multiple opportunities by their CRM IDs\n *\n * @param array<string> $crmIds Array of HubSpot deal IDs (max 100)\n * @param array<string> $fields Array of property names to fetch\n *\n * @return array<string, array> Array keyed by CRM ID with opportunity data\n */\n public function getOpportunitiesByIds(array $crmIds, array $fields): array\n {\n return $this->batchReadObjects('deals', $crmIds, $fields);\n }\n\n /**\n * Batch read multiple companies by their CRM IDs\n *\n * @param array<string> $crmIds Array of HubSpot company IDs (max 100)\n * @param array<string> $fields Array of property names to fetch\n *\n * @return array<string, array> Array keyed by CRM ID with company data\n */\n public function getCompaniesByIds(array $crmIds, array $fields): array\n {\n return $this->batchReadObjects('companies', $crmIds, $fields);\n }\n\n /**\n * Batch read multiple contacts by their CRM IDs\n *\n * @param array<string> $crmIds Array of HubSpot contact IDs (max 100)\n * @param array<string> $fields Array of property names to fetch\n *\n * @return array<string, array> Array keyed by CRM ID with contact data\n */\n public function getContactsByIds(array $crmIds, array $fields): array\n {\n return $this->batchReadObjects('contacts', $crmIds, $fields);\n }\n\n /**\n * @throws CompanyApiException\n * @throws CrmException\n */\n public function getAccountById(string $crmId, array $fields): array\n {\n try {\n $company = $this->getNewInstance()->crm()->companies()->basicApi()->getById(\n $crmId,\n implode(',', $fields),\n );\n } catch (CompanyApiException $e) {\n $this->log->info('[Hubspot] Failed to fetch account', [\n 'crm_id' => $crmId,\n 'reason' => $e->getMessage(),\n ]);\n\n throw $e;\n }\n\n if (! $company instanceof CompaniesWithAssociations) {\n throw new CrmException('Account not found');\n }\n\n return [\n 'id' => $company->getId(),\n 'properties' => $company->getProperties(),\n ];\n }\n\n /**\n * @throws ContactApiException\n * @throws CrmException\n */\n public function getContactById(string $crmId, array $fields): array\n {\n try {\n $contact = $this->getNewInstance()->crm()->contacts()->basicApi()->getById(\n $crmId,\n implode(',', $fields)\n );\n } catch (ContactApiException $e) {\n $this->log->info('[Hubspot] Failed to fetch contact', [\n 'crm_id' => $crmId,\n 'reason' => $e->getMessage(),\n ]);\n\n throw $e;\n }\n\n if (! $contact instanceof ContactsWithAssociations) {\n throw new CrmException('Contact not found');\n }\n\n return [\n 'id' => $contact->getId(),\n 'properties' => $contact->getProperties(),\n ];\n }\n\n /**\n * This is email search request that Hubspot offers as GET (more generous quota)\n */\n public function getContactByEmail(string $email, array $fields = []): array\n {\n try {\n $contact = $this->getNewInstance()->crm()->contacts()->basicApi()->getById(\n $email,\n implode(',', $fields),\n null,\n false,\n 'email'\n );\n\n return [\n 'id' => $contact->getId(),\n 'properties' => $contact->getProperties(),\n ];\n } catch (ContactApiException $e) {\n $this->log->info('[Hubspot] Failed to fetch contact', [\n 'email' => $email,\n 'reason' => $e->getMessage(),\n ]);\n\n return [];\n }\n }\n\n /**\n * @throws CrmException\n */\n public function fetchProperty(string $objectType, string $propertyId): Property\n {\n $result = $this->getNewInstance()->crm()->properties()->coreApi()->getByName($objectType, $propertyId);\n\n if (! $result instanceof Property) {\n $this->log->error('[Hubspot] Failed to fetch property', [\n 'object_type' => $objectType,\n 'property_id' => $propertyId,\n 'reason' => $result->getMessage(),\n ]);\n\n throw new CrmException('Failed to fetch property');\n }\n\n return $result;\n }\n\n /**\n * @return array<CrmFieldOption>\n */\n public function fetchPropertyOptions(string $objectType, string $propertyId): array\n {\n /** @var array<CrmFieldOption> */\n return $this->fetchProperty($objectType, $propertyId)->getOptions();\n }\n\n /**\n * @return array<array{id:string, label:string, deleted:bool}>\n */\n public function fetchCallDispositions(): array\n {\n /** @var Response $response */\n $response = $this->getInstance()->engagements()->getCallDispositions();\n\n /**\n * @var array<array{\n * id:string,\n * label:string,\n * deleted: bool\n * }>\n */\n return $response->toArray();\n }\n\n /**\n * @return array<CrmFieldOption>\n */\n public function fetchOpportunityPipelineStages(): array\n {\n $stages = [];\n $apiResponse = $this->getNewInstance()->crm()->pipelines()->pipelinesApi()->getAll('deals');\n\n if ($apiResponse instanceof Error) {\n $this->log->error('[Hubspot] Failed to fetch opportunity pipelines', [\n 'reason' => $apiResponse->getMessage(),\n ]);\n\n return [];\n }\n\n foreach ($apiResponse->getResults() as $pipeline) {\n $pipelineStages = array_map(\n static function (PipelineStage $stage) {\n return [\n 'id' => $stage->getId(),\n 'label' => $stage->getLabel(),\n ];\n },\n $pipeline->getStages()\n );\n\n $stages = array_merge($stages, $pipelineStages);\n }\n\n return $stages;\n }\n\n public function fetchOpportunityPipelines(): array\n {\n $pipelines = [];\n\n try {\n $apiResponse = $this->makeRequest('/crm/v3/pipelines/deals');\n } catch (\\Exception $e) {\n $this->log->info('[Hubspot] Failed to fetch opportunity pipelines', [\n 'reason' => $e->getMessage(),\n ]);\n\n return [];\n }\n\n $response = $apiResponse->toArray();\n\n foreach ($response['results'] as $pipeline) {\n $pipelines[] = [\n 'id' => $pipeline['id'],\n 'label' => $pipeline['label'],\n ];\n }\n\n return $pipelines;\n }\n\n /**\n * @return array<CrmFieldOption>\n */\n public function fetchMeetingOutcomeFieldOptions(Field $field): array\n {\n return $field->getCrmProviderId() === 'meetingOutcome'\n ? $this->fetchMeetingOutcomeTypes()\n : $this->fetchCallActivityTypes();\n }\n\n public function fetchMeetingOutcomeTypes(): array\n {\n return $this->extractMeetingTypeOptions(\n 'https://api.hubapi.com/crm/v3/properties/meeting/hs_meeting_outcome'\n );\n }\n\n public function fetchCallActivityTypes(): array\n {\n return $this->extractMeetingTypeOptions(\n 'https://api.hubapi.com/crm/v3/properties/call/hs_activity_type'\n );\n }\n\n private function extractMeetingTypeOptions(string $endpoint): array\n {\n /** @var Response $response */\n $response = $this->getInstance()\n ->getClient()\n ->request('GET', $endpoint);\n\n /**\n * @var array<array{\n * value: string,\n * label: string,\n * displayOrder: int\n * }> $optionData\n */\n $optionData = $response->toArray()['options'] ?? [];\n\n $options = [];\n foreach ($optionData as $item) {\n $options[] = [\n 'id' => $item['value'],\n 'value' => $item['value'],\n 'label' => $item['label'],\n 'display_order' => $item['displayOrder'],\n ];\n }\n\n return $options;\n }\n\n /**\n * @return array<CrmFieldOption>\n */\n public function fetchDispositionFieldOptions(): array\n {\n $options = [];\n\n $dispositions = $this->fetchCallDispositions();\n\n foreach ($dispositions as $disposition) {\n if ($disposition['deleted'] !== false) {\n continue;\n }\n\n $option['value'] = $disposition['id'];\n $option['id'] = $disposition['id'];\n $option['label'] = $disposition['label'];\n\n $options[] = $option;\n }\n\n return $options;\n }\n\n /**\n * @return array<CrmFieldOption>\n */\n public function fetchOpportunityFieldOptions(Field $field): array\n {\n if ($field->isStageField()) {\n return $this->fetchOpportunityPipelineStages();\n }\n\n if ($field->isPipelineField()) {\n return $this->fetchOpportunityPipelines();\n }\n\n return $this->fetchPropertyOptions('deals', $field->getCrmProviderId());\n }\n\n /**\n * @throws BadRequest\n * @throws HubspotException\n */\n public function makeRequest(string $endpoint, $method = 'GET', $payload = [], ?string $queryString = null)\n {\n $endpoint = self::BASE_URL . $endpoint;\n\n if ($method === 'GET') {\n return $this->getInstance()->getClient()?->request(\n method: $method,\n endpoint: $endpoint,\n query_string: $queryString\n );\n } else {\n return $this->getInstance()->getClient()->request($method, $endpoint, [\n 'json' => ($payload),\n ]);\n }\n }\n\n /**\n * @throws BadRequest\n * @throws HubspotException\n */\n public function createMeeting(array $payload): Response\n {\n $endpoint = '/crm/v3/objects/meetings';\n\n return $this->makeRequest($endpoint, 'POST', $payload);\n }\n\n /**\n * @throws BadRequest\n * @throws HubspotException\n */\n public function updateMeeting(string $meetingId, array $payload): Response\n {\n $endpoint = '/crm/v3/objects/meetings/' . $meetingId;\n\n return $this->makeRequest($endpoint, 'PATCH', $payload);\n }\n\n /**\n * @throws \\Exception\n */\n public function createNote(\n string $body,\n string $ownerId,\n int $timestamp,\n string $objectId,\n NoteObject $noteObject\n ): ?string {\n try {\n $noteInput = new SimplePublicObjectInput([\n 'properties' => [\n 'hs_note_body' => $body,\n 'hubspot_owner_id' => $ownerId,\n 'hs_timestamp' => $timestamp,\n ],\n ]);\n\n // Create note\n $note = $this->getNewInstance()->crm()->objects()->basicApi()->create('note', $noteInput);\n\n $this->getNewInstance()->crm()->objects()->associationsApi()->create(\n 'note',\n $note->getId(),\n $this->getNoteObject($noteObject),\n $objectId,\n $this->getNoteAssociationType($noteObject),\n );\n\n return $note->getId();\n } catch (\\Exception $e) {\n $this->log->error('[Hubspot] Failed to create note', [\n 'objectId' => $objectId,\n 'noteObject' => $noteObject->getObjectType(),\n 'reason' => $e->getMessage(),\n ]);\n\n \\Sentry::captureException($e);\n }\n\n return null;\n }\n\n public function updateEngagement(string $objectId, array $engagement, array $metadata): void\n {\n $this->getInstance()->engagements()->update($objectId, $engagement, $metadata);\n }\n\n public function getEngagementData(string $engagementId): array\n {\n $engagement = $this->getInstance()->engagements()->get($engagementId);\n\n return $engagement->toArray();\n }\n\n public function createEngagement(array $engagement, array $associations, array $metadata): Response\n {\n return $this->getInstance()\n ->engagements()\n ->create($engagement, $associations, $metadata);\n }\n\n public function isUnauthorizedException(\\Exception $e): bool\n {\n // Check for specific HubSpot API exception types first\n if ($e instanceof BadRequest) {\n // BadRequest can contain 401 status codes\n return $e->getCode() === 401;\n }\n\n // Check for HTTP client exceptions with status codes\n if ($e instanceof \\GuzzleHttp\\Exception\\RequestException && $e->hasResponse()) {\n $response = $e->getResponse();\n if ($response !== null) {\n return $response->getStatusCode() === 401;\n }\n }\n\n // Check for Guzzle HTTP exceptions\n if ($e instanceof \\GuzzleHttp\\Exception\\ClientException) {\n return $e->getCode() === 401;\n }\n\n // Fallback to string matching as last resort, but be more specific\n $message = strtolower($e->getMessage());\n\n return str_contains($message, '401 unauthorized') ||\n str_contains($message, 'http 401') ||\n str_contains($message, 'status code 401') ||\n (preg_match('/\\b401\\b/', $message) && str_contains($message, 'unauthorized'));\n }\n\n /**\n * Validates and refreshes the access token if needed before API requests.\n * This ensures long-running processes don't fail due to token expiration.\n *\n * @throws SocialAccountTokenInvalidException\n */\n public function ensureValidToken(): void\n {\n if ($this->oauthAccount === null) {\n return;\n }\n\n $newToken = $this->tokenManager->ensureValidToken($this->oauthAccount);\n if ($newToken !== null) {\n $this->accessToken = $newToken;\n }\n }\n\n public function getConfig()\n {\n return $this->config;\n }\n\n // returns only active (archived=false)\n public function getOwners(): array\n {\n return $this->getNewInstance()->crm()->owners()->getAll();\n }\n\n /**\n * @param bool $archived\n *\n * @return array<Owner>|[]\n */\n public function getOwnersArchived(bool $archived = true): array\n {\n $endpoint = '/crm/v3/owners';\n $queryParams = [\n 'archived' => $archived ? 'true' : 'false',\n ];\n $queryString = http_build_query($queryParams);\n\n $owners = [];\n\n try {\n $response = $this->makeRequest(endpoint: $endpoint, queryString: $queryString);\n $responseData = $response?->toArray();\n\n foreach ($responseData['results'] as $result) {\n try {\n $owners[] = Owner::create($result);\n } catch (Throwable $e) {\n $this->log->error('[HubSpot] Failed to process owner data', [\n 'result' => $result,\n 'error' => $e->getMessage(),\n ]);\n\n continue;\n }\n }\n } catch (Throwable $e) {\n $this->log->error('HubSpot] Failed to fetch owners', [\n 'archived' => $archived,\n 'error' => $e->getMessage(),\n ]);\n\n return [];\n }\n\n return $owners;\n }\n\n public function getMeeting(string $engagementId): ObjectWithAssociations\n {\n return $this->getNewInstance()->crm()->objects()->basicApi()\n ->getById('meeting', $engagementId, null, 'contact,company,deal');\n }\n\n public function deleteEngagement(string $engagementId): void\n {\n $this->getInstance()->engagements()->delete((int) $engagementId);\n }\n\n public function getAssociationsData(array $ids, string $fromObject, string $toObject): array\n {\n $associationData = [];\n $idChunks = array_chunk($ids, self::ASSOCIATIONS_BATCH_SIZE_LIMIT);\n\n foreach ($idChunks as $idChunk) {\n try {\n $batchInput = new \\HubSpot\\Client\\Crm\\Associations\\Model\\BatchInputPublicObjectId();\n $batchInput->setInputs(array_map(function ($id) {\n $publicObjectId = new \\HubSpot\\Client\\Crm\\Associations\\Model\\PublicObjectId();\n $publicObjectId->setId($id);\n\n return $publicObjectId;\n }, $idChunk));\n\n $associatedObjectsData = $this\n ->getNewInstance()\n ->crm()\n ->associations()\n ->batchApi()\n ->read($fromObject, $toObject, $batchInput);\n\n if ($associatedObjectsData instanceof \\HubSpot\\Client\\Crm\\Associations\\Model\\BatchResponsePublicAssociationMulti) {\n foreach ($associatedObjectsData->getResults() as $association) {\n $from = $association->getFrom()->getId();\n $toAssociations = $association->getTo();\n\n if (! empty($toAssociations)) {\n $associationData[$from] = array_map(function ($item) {\n return $item->getId();\n }, $toAssociations);\n }\n }\n }\n } catch (\\Exception $e) {\n $this->log->error('[Hubspot] Failed to fetch associations', [\n 'from_object' => $fromObject,\n 'to_object' => $toObject,\n 'reason' => $e->getMessage(),\n ]);\n }\n }\n\n return $associationData;\n }\n\n /**\n * @throws \\Exception\n */\n private function getNoteAssociationType(NoteObject $noteObject): string\n {\n return match($noteObject) {\n NoteObject::Opportunity => 'note_to_deal',\n NoteObject::Lead, NoteObject::Contact => 'note_to_contact', // or 'note_to_lead' if your portal supports it\n NoteObject::Account => 'note_to_company',\n NoteObject::Call, NoteObject::Event => throw new \\Exception('Not supported'),\n };\n }\n\n /**\n * @throws \\Exception\n */\n private function getNoteObject(NoteObject $noteObject): string\n {\n return match($noteObject) {\n NoteObject::Opportunity => 'deal',\n NoteObject::Lead, NoteObject::Contact => 'contact',\n NoteObject::Account => 'company',\n NoteObject::Call, NoteObject::Event => throw new \\Exception('Not supported'),\n };\n }\n\n public function addAssociations(string $objectType, string $associationType, array $payload): Response\n {\n $endpoint = \"/crm/v4/associations/$objectType/$associationType/batch/create\";\n\n return $this->makeRequest($endpoint, 'POST', $payload);\n }\n\n public function removeAssociations(string $objectType, string $associationType, array $payload): Response\n {\n $endpoint = \"/crm/v4/associations/$objectType/$associationType/batch/archive\";\n\n return $this->makeRequest($endpoint, 'POST', $payload);\n }\n}","role_description":"text entry area","is_enabled":true,"is_focused":true,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Project","depth":3,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Project","depth":3,"bounds":{"left":0.011968086,"top":0.047885075,"width":0.024268618,"height":0.024740623},"on_screen":true,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"New File or Directory…","depth":4,"bounds":{"left":0.27027926,"top":1.0,"width":0.008643617,"height":0.0},"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Expand Selected","depth":4,"bounds":{"left":0.27027926,"top":1.0,"width":0.008643617,"height":0.0},"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Collapse All","depth":4,"bounds":{"left":0.27027926,"top":1.0,"width":0.008643617,"height":0.0},"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Options","depth":4,"bounds":{"left":0.27027926,"top":1.0,"width":0.008643617,"height":0.0},"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Hide","depth":4,"bounds":{"left":0.27027926,"top":1.0,"width":0.008643617,"height":0.0},"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false}]...
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# [PASSWORD_DOTS] HS [PASSWORD_DOTS]
select * from teams where id = 2; # 2
select * from features; # 2
select * from team_features where team_id = 2; # 2
select * from crm_configurations where id = 2; # 2
select * from users where team_id = 2; #
select * from playbooks where team_id = 2; # event 38
select * from playbook_categories where playbook_id = 38; #
SELECT * FROM activities WHERE crm_configuration_id = 2 and crm_provider_id is not null order by id desc;
[URL_WITH_CREDENTIALS] CrmFieldOption array{id:string, label:string, value?:string}
*/
class Client extends BaseClient implements HubspotClientInterface
{
public const string MIN_API_VERSION = '2';
public const string BASE_URL = '[URL_WITH_CREDENTIALS] T
* @param callable(): T $apiCall
* @return T
*
* @throws RateLimitException
*/
private function executeRequest(callable $apiCall)
{
if (! $this->rateLimiter->canMakeRequest($this->config)) {
$retryAfter = $this->rateLimiter->requestAvailableIn($this->config);
$this->log->warning('[Hubspot] Rate limit exceeded, deferring request', [
'team_id' => $this->config->team_id,
'config_id' => $this->config->getId(),
'retry_after' => $retryAfter,
]);
throw new RateLimitException(
'Hubspot rate limit reached for configuration ' . $this->config->getId(),
$retryAfter,
);
}
$this->rateLimiter->incrementRequestCount($this->config);
try {
return $apiCall();
} catch (Throwable $e) {
if ($this->isHubspotRateLimit($e)) {
$retryAfter = $this->parseRetryAfter($e);
$this->log->warning('[Hubspot] Received 429 from API', [
'team_id' => $this->config->team_id,
'config_id' => $this->config->getId(),
'retry_after' => $retryAfter,
'reason' => $e->getMessage(),
]);
throw new RateLimitException('Hubspot returned 429', $retryAfter, $e);
}
throw $e;
}
}
private function isHubspotRateLimit(Throwable $e): bool
{
return method_exists($e, 'getCode') && (int) $e->getCode() === 429;
}
private function parseRetryAfter(Throwable $e): int
{
if (method_exists($e, 'getResponseHeaders')) {
$headers = $e->getResponseHeaders() ?: [];
$value = $headers['Retry-After'] ?? $headers['retry-after'] ?? null;
if (is_array($value)) {
$value = $value[0] ?? null;
}
if (is_numeric($value)) {
return (int) $value;
}
}
return 10;
}
public function getMinimumApiVersion(): string
{
return self::MIN_API_VERSION;
}
public function getInstance(): Factory
{
return new Factory([
'key' => $this->accessToken,
'oauth2' => true,
'base_url' => $this->baseUrl,
]);
}
public function getNewInstance(): Discovery
{
return \HubSpot\Factory::createWithAccessToken($this->accessToken);
}
/**
* Secondly and daily limits for Hubspot API
*
* Product Tier: Free & Starter | Professional & Enterprise | API add-on (any tier)
* Burst: 100/10 seconds | 150/10 seconds | 200/10 seconds
* Daily: 250,000 | 500,000 | 1,000,000
*
* Official documentation states: The search endpoints are rate limited to five requests per second.
* Since with 5 RPS were still hitting secondly rate limits we lowered it to 4
*/
public function getPaginatedData(array $payload, string $type, int $offset = 0): array
{
$total = 0;
$lastId = null;
$rows = [];
foreach ($this->getPaginatedDataGenerator($payload, $type, $offset, $total, $lastId) as $row) {
$rows[] = $row;
}
return ['results' => $rows, 'total' => $total, 'last_record' => $lastId];
}
/**
* @throws HubspotException
* @throws SocialAccountTokenInvalidException
* @throws BadRequest
*/
public function getPaginatedDataGenerator(
array $payload,
string $type,
int $offset = 0,
int &$total = 0,
?string &$lastRecordId = null
): \Generator {
return $this->paginationService->getPaginatedDataGenerator(
$this,
$payload,
$type,
$offset,
$total,
$lastRecordId
);
}
/**
* @throws DealApiException
* @throws CrmException
*/
public function getOpportunityById(string $crmId, array $fields): array
{
try {
$deal = $this->executeRequest(fn () => $this->getNewInstance()->crm()->deals()->basicApi()->getById(
$crmId,
implode(',', $fields),
'companies,contacts'
));
} catch (DealApiException $e) {
$this->log->info('[Hubspot] Failed to fetch opportunity', [
'crm_id' => $crmId,
'reason' => $e->getMessage(),
]);
throw $e;
}
if (! $deal instanceof DealWithAssociations) {
throw new CrmException('Deal not found');
}
return [
'id' => $deal->getId(),
'properties' => $deal->getProperties(),
'associations' => $deal->getAssociations(),
];
}
/**
* Generic batch read method for HubSpot objects
*
* @param string $objectType The object type ('deals', 'companies', 'contacts')
* @param array<string> $crmIds Array of HubSpot object IDs (max 100)
* @param array<string> $fields Array of property names to fetch
*
* @return array<string, array> Array keyed by CRM ID with object data
*/
private function batchReadObjects(string $objectType, array $crmIds, array $fields): array
{
if (empty($crmIds)) {
return [];
}
$this->validateBatchSize($objectType, $crmIds);
$this->ensureValidToken();
try {
$batchConfig = $this->createBatchConfiguration($objectType);
$batchReadRequest = $this->prepareBatchRequest($batchConfig, $crmIds, $fields);
$response = $batchConfig['api']->read($batchReadRequest);
$this->validateApiResponse($response, $objectType);
$results = $this->processApiResults($response);
$this->logBatchResults($objectType, $crmIds, $results);
return $results;
} catch (\Throwable $e) {
$this->handleBatchError($e, $objectType, $crmIds);
}
}
private function validateBatchSize(string $objectType, array $crmIds): void
{
if (count($crmIds) > 100) {
throw new \InvalidArgumentException("Batch size cannot exceed 100 {$objectType}");
}
}
private function createBatchConfiguration(string $objectType): array
{
$configurations = [
'deals' => [
'batchReadRequest' => new \HubSpot\Client\Crm\Deals\Model\BatchReadInputSimplePublicObjectId(),
'inputClass' => \HubSpot\Client\Crm\Deals\Model\SimplePublicObjectId::class,
'api' => $this->getNewInstance()->crm()->deals()->batchApi(),
],
'companies' => [
'batchReadRequest' => new \HubSpot\Client\Crm\Companies\Model\BatchReadInputSimplePublicObjectId(),
'inputClass' => \HubSpot\Client\Crm\Companies\Model\SimplePublicObjectId::class,
'api' => $this->getNewInstance()->crm()->companies()->batchApi(),
],
'contacts' => [
'batchReadRequest' => new \HubSpot\Client\Crm\Contacts\Model\BatchReadInputSimplePublicObjectId(),
'inputClass' => \HubSpot\Client\Crm\Contacts\Model\SimplePublicObjectId::class,
'api' => $this->getNewInstance()->crm()->contacts()->batchApi(),
],
];
if (! isset($configurations[$objectType])) {
throw new \InvalidArgumentException("Unsupported object type: {$objectType}");
}
return $configurations[$objectType];
}
private function prepareBatchRequest(array $batchConfig, array $crmIds, array $fields): object
{
$batchReadRequest = $batchConfig['batchReadRequest'];
$inputClass = $batchConfig['inputClass'];
$inputs = array_map(function ($crmId) use ($inputClass) {
$input = new $inputClass();
$input->setId($crmId);
return $input;
}, $crmIds);
$batchReadRequest->setInputs($inputs);
$batchReadRequest->setProperties($fields);
return $batchReadRequest;
}
private function validateApiResponse($response, string $objectType): void
{
if (! $response) {
throw new CrmException("HubSpot API returned null response for {$objectType} batch read");
}
}
private function processApiResults($response): array
{
$results = [];
$responseResults = $response->getResults();
if ($responseResults) {
foreach ($responseResults as $object) {
if ($object && $object->getId()) {
$results[$object->getId()] = [
'id' => $object->getId(),
'properties' => $object->getProperties() ?: [],
];
}
}
}
return $results;
}
private function logBatchResults(string $objectType, array $crmIds, array $results): void
{
$this->log->info("[HubSpot] Batch fetched {$objectType}", [
'requested_count' => count($crmIds),
'returned_count' => count($results),
'crm_ids' => $crmIds,
]);
}
private function handleBatchError(\Throwable $e, string $objectType, array $crmIds): void
{
$errorMessage = $e->getMessage() ?: 'Unknown error';
$errorTrace = $e->getTraceAsString() ?: 'No trace available';
$this->log->error("[HubSpot] Failed to batch fetch {$objectType}", [
'crm_ids' => $crmIds,
'error' => $errorMessage,
'trace' => $errorTrace,
]);
throw new CrmException("Failed to batch fetch {$objectType}: " . $errorMessage);
}
/**
* Batch read multiple opportunities by their CRM IDs
*
* @param array<string> $crmIds Array of HubSpot deal IDs (max 100)
* @param array<string> $fields Array of property names to fetch
*
* @return array<string, array> Array keyed by CRM ID with opportunity data
*/
public function getOpportunitiesByIds(array $crmIds, array $fields): array
{
return $this->batchReadObjects('deals', $crmIds, $fields);
}
/**
* Batch read multiple companies by their CRM IDs
*
* @param array<string> $crmIds Array of HubSpot company IDs (max 100)
* @param array<string> $fields Array of property names to fetch
*
* @return array<string, array> Array keyed by CRM ID with company data
*/
public function getCompaniesByIds(array $crmIds, array $fields): array
{
return $this->batchReadObjects('companies', $crmIds, $fields);
}
/**
* Batch read multiple contacts by their CRM IDs
*
* @param array<string> $crmIds Array of HubSpot contact IDs (max 100)
* @param array<string> $fields Array of property names to fetch
*
* @return array<string, array> Array keyed by CRM ID with contact data
*/
public function getContactsByIds(array $crmIds, array $fields): array
{
return $this->batchReadObjects('contacts', $crmIds, $fields);
}
/**
* @throws CompanyApiException
* @throws CrmException
*/
public function getAccountById(string $crmId, array $fields): array
{
try {
$company = $this->getNewInstance()->crm()->companies()->basicApi()->getById(
$crmId,
implode(',', $fields),
);
} catch (CompanyApiException $e) {
$this->log->info('[Hubspot] Failed to fetch account', [
'crm_id' => $crmId,
'reason' => $e->getMessage(),
]);
throw $e;
}
if (! $company instanceof CompaniesWithAssociations) {
throw new CrmException('Account not found');
}
return [
'id' => $company->getId(),
'properties' => $company->getProperties(),
];
}
/**
* @throws ContactApiException
* @throws CrmException
*/
public function getContactById(string $crmId, array $fields): array
{
try {
$contact = $this->getNewInstance()->crm()->contacts()->basicApi()->getById(
$crmId,
implode(',', $fields)
);
} catch (ContactApiException $e) {
$this->log->info('[Hubspot] Failed to fetch contact', [
'crm_id' => $crmId,
'reason' => $e->getMessage(),
]);
throw $e;
}
if (! $contact instanceof ContactsWithAssociations) {
throw new CrmException('Contact not found');
}
return [
'id' => $contact->getId(),
'properties' => $contact->getProperties(),
];
}
/**
* This is email search request that Hubspot offers as GET (more generous quota)
*/
public function getContactByEmail(string $email, array $fields = []): array
{
try {
$contact = $this->getNewInstance()->crm()->contacts()->basicApi()->getById(
$email,
implode(',', $fields),
null,
false,
'email'
);
return [
'id' => $contact->getId(),
'properties' => $contact->getProperties(),
];
} catch (ContactApiException $e) {
$this->log->info('[Hubspot] Failed to fetch contact', [
'email' => $email,
'reason' => $e->getMessage(),
]);
return [];
}
}
/**
* @throws CrmException
*/
public function fetchProperty(string $objectType, string $propertyId): Property
{
$result = $this->getNewInstance()->crm()->properties()->coreApi()->getByName($objectType, $propertyId);
if (! $result instanceof Property) {
$this->log->error('[Hubspot] Failed to fetch property', [
'object_type' => $objectType,
'property_id' => $propertyId,
'reason' => $result->getMessage(),
]);
throw new CrmException('Failed to fetch property');
}
return $result;
}
/**
* @return array<CrmFieldOption>
*/
public function fetchPropertyOptions(string $objectType, string $propertyId): array
{
/** @var array<CrmFieldOption> */
return $this->fetchProperty($objectType, $propertyId)->getOptions();
}
/**
* @return array<array{id:string, label:string, deleted:bool}>
*/
public function fetchCallDispositions(): array
{
/** @var Response $response */
$response = $this->getInstance()->engagements()->getCallDispositions();
/**
* @var array<array{
* id:string,
* label:string,
* deleted: bool
* }>
*/
return $response->toArray();
}
/**
* @return array<CrmFieldOption>
*/
public function fetchOpportunityPipelineStages(): array
{
$stages = [];
$apiResponse = $this->getNewInstance()->crm()->pipelines()->pipelinesApi()->getAll('deals');
if ($apiResponse instanceof Error) {
$this->log->error('[Hubspot] Failed to fetch opportunity pipelines', [
'reason' => $apiResponse->getMessage(),
]);
return [];
}
foreach ($apiResponse->getResults() as $pipeline) {
$pipelineStages = array_map(
static function (PipelineStage $stage) {
return [
'id' => $stage->getId(),
'label' => $stage->getLabel(),
];
},
$pipeline->getStages()
);
$stages = array_merge($stages, $pipelineStages);
}
return $stages;
}
public function fetchOpportunityPipelines(): array
{
$pipelines = [];
try {
$apiResponse = $this->makeRequest('/crm/v3/pipelines/deals');
} catch (\Exception $e) {
$this->log->info('[Hubspot] Failed to fetch opportunity pipelines', [
'reason' => $e->getMessage(),
]);
return [];
}
$response = $apiResponse->toArray();
foreach ($response['results'] as $pipeline) {
$pipelines[] = [
'id' => $pipeline['id'],
'label' => $pipeline['label'],
];
}
return $pipelines;
}
/**
* @return array<CrmFieldOption>
*/
public function fetchMeetingOutcomeFieldOptions(Field $field): array
{
return $field->getCrmProviderId() === 'meetingOutcome'
? $this->fetchMeetingOutcomeTypes()
: $this->fetchCallActivityTypes();
}
public function fetchMeetingOutcomeTypes(): array
{
return $this->extractMeetingTypeOptions(
'[URL_WITH_CREDENTIALS] Response $response */
$response = $this->getInstance()
->getClient()
->request('GET', $endpoint);
/**
* @var array<array{
* value: string,
* label: string,
* displayOrder: int
* }> $optionData
*/
$optionData = $response->toArray()['options'] ?? [];
$options = [];
foreach ($optionData as $item) {
$options[] = [
'id' => $item['value'],
'value' => $item['value'],
'label' => $item['label'],
'display_order' => $item['displayOrder'],
];
}
return $options;
}
/**
* @return array<CrmFieldOption>
*/
public function fetchDispositionFieldOptions(): array
{
$options = [];
$dispositions = $this->fetchCallDispositions();
foreach ($dispositions as $disposition) {
if ($disposition['deleted'] !== false) {
continue;
}
$option['value'] = $disposition['id'];
$option['id'] = $disposition['id'];
$option['label'] = $disposition['label'];
$options[] = $option;
}
return $options;
}
/**
* @return array<CrmFieldOption>
*/
public function fetchOpportunityFieldOptions(Field $field): array
{
if ($field->isStageField()) {
return $this->fetchOpportunityPipelineStages();
}
if ($field->isPipelineField()) {
return $this->fetchOpportunityPipelines();
}
return $this->fetchPropertyOptions('deals', $field->getCrmProviderId());
}
/**
* @throws BadRequest
* @throws HubspotException
*/
public function makeRequest(string $endpoint, $method = 'GET', $payload = [], ?string $queryString = null)
{
$endpoint = self::BASE_URL . $endpoint;
if ($method === 'GET') {
return $this->getInstance()->getClient()?->request(
method: $method,
endpoint: $endpoint,
query_string: $queryString
);
} else {
return $this->getInstance()->getClient()->request($method, $endpoint, [
'json' => ($payload),
]);
}
}
/**
* @throws BadRequest
* @throws HubspotException
*/
public function createMeeting(array $payload): Response
{
$endpoint = '/crm/v3/objects/meetings';
return $this->makeRequest($endpoint, 'POST', $payload);
}
/**
* @throws BadRequest
* @throws HubspotException
*/
public function updateMeeting(string $meetingId, array $payload): Response
{
$endpoint = '/crm/v3/objects/meetings/' . $meetingId;
return $this->makeRequest($endpoint, 'PATCH', $payload);
}
/**
* @throws \Exception
*/
public function createNote(
string $body,
string $ownerId,
int $timestamp,
string $objectId,
NoteObject $noteObject
): ?string {
try {
$noteInput = new SimplePublicObjectInput([
'properties' => [
'hs_note_body' => $body,
'hubspot_owner_id' => $ownerId,
'hs_timestamp' => $timestamp,
],
]);
// Create note
$note = $this->getNewInstance()->crm()->objects()->basicApi()->create('note', $noteInput);
$this->getNewInstance()->crm()->objects()->associationsApi()->create(
'note',
$note->getId(),
$this->getNoteObject($noteObject),
$objectId,
$this->getNoteAssociationType($noteObject),
);
return $note->getId();
} catch (\Exception $e) {
$this->log->error('[Hubspot] Failed to create note', [
'objectId' => $objectId,
'noteObject' => $noteObject->getObjectType(),
'reason' => $e->getMessage(),
]);
\Sentry::captureException($e);
}
return null;
}
public function updateEngagement(string $objectId, array $engagement, array $metadata): void
{
$this->getInstance()->engagements()->update($objectId, $engagement, $metadata);
}
public function getEngagementData(string $engagementId): array
{
$engagement = $this->getInstance()->engagements()->get($engagementId);
return $engagement->toArray();
}
public function createEngagement(array $engagement, array $associations, array $metadata): Response
{
return $this->getInstance()
->engagements()
->create($engagement, $associations, $metadata);
}
public function isUnauthorizedException(\Exception $e): bool
{
// Check for specific HubSpot API exception types first
if ($e instanceof BadRequest) {
// BadRequest can contain 401 status codes
return $e->getCode() === 401;
}
// Check for HTTP client exceptions with status codes
if ($e instanceof \GuzzleHttp\Exception\RequestException && $e->hasResponse()) {
$response = $e->getResponse();
if ($response !== null) {
return $response->getStatusCode() === 401;
}
}
// Check for Guzzle HTTP exceptions
if ($e instanceof \GuzzleHttp\Exception\ClientException) {
return $e->getCode() === 401;
}
// Fallback to string matching as last resort, but be more specific
$message = strtolower($e->getMessage());
return str_contains($message, '401 unauthorized') ||
str_contains($message, 'http 401') ||
str_contains($message, 'status code 401') ||
(preg_match('/\b401\b/', $message) && str_contains($message, 'unauthorized'));
}
/**
* Validates and refreshes the access token if needed before API requests.
* This ensures long-running processes don't fail due to token expiration.
*
* @throws SocialAccountTokenInvalidException
*/
public function ensureValidToken(): void
{
if ($this->oauthAccount === null) {
return;
}
$newToken = $this->tokenManager->ensureValidToken($this->oauthAccount);
if ($newToken !== null) {
$this->accessToken = $newToken;
}
}
public function getConfig()
{
return $this->config;
}
// returns only active (archived=false)
public function getOwners(): array
{
return $this->getNewInstance()->crm()->owners()->getAll();
}
/**
* @param bool $archived
*
* @return array<Owner>|[]
*/
public function getOwnersArchived(bool $archived = true): array
{
$endpoint = '/crm/v3/owners';
$queryParams = [
'archived' => $archived ? 'true' : 'false',
];
$queryString = http_build_query($queryParams);
$owners = [];
try {
$response = $this->makeRequest(endpoint: $endpoint, queryString: $queryString);
$responseData = $response?->toArray();
foreach ($responseData['results'] as $result) {
try {
$owners[] = Owner::create($result);
} catch (Throwable $e) {
$this->log->error('[HubSpot] Failed to process owner data', [
'result' => $result,
'error' => $e->getMessage(),
]);
continue;
}
}
} catch (Throwable $e) {
$this->log->error('HubSpot] Failed to fetch owners', [
'archived' => $archived,
'error' => $e->getMessage(),
]);
return [];
}
return $owners;
}
public function getMeeting(string $engagementId): ObjectWithAssociations
{
return $this->getNewInstance()->crm()->objects()->basicApi()
->getById('meeting', $engagementId, null, 'contact,company,deal');
}
public function deleteEngagement(string $engagementId): void
{
$this->getInstance()->engagements()->delete((int) $engagementId);
}
public function getAssociationsData(array $ids, string $fromObject, string $toObject): array
{
$associationData = [];
$idChunks = array_chunk($ids, self::ASSOCIATIONS_BATCH_SIZE_LIMIT);
foreach ($idChunks as $idChunk) {
try {
$batchInput = new \HubSpot\Client\Crm\Associations\Model\BatchInputPublicObjectId();
$batchInput->setInputs(array_map(function ($id) {
$publicObjectId = new \HubSpot\Client\Crm\Associations\Model\PublicObjectId();
$publicObjectId->setId($id);
return $publicObjectId;
}, $idChunk));
$associatedObjectsData = $this
->getNewInstance()
->crm()
->associations()
->batchApi()
->read($fromObject, $toObject, $batchInput);
if ($associatedObjectsData instanceof \HubSpot\Client\Crm\Associations\Model\BatchResponsePublicAssociationMulti) {
foreach ($associatedObjectsData->getResults() as $association) {
$from = $association->getFrom()->getId();
$toAssociations = $association->getTo();
if (! empty($toAssociations)) {
$associationData[$from] = array_map(function ($item) {
return $item->getId();
}, $toAssociations);
}
}
}
} catch (\Exception $e) {
$this->log->error('[Hubspot] Failed to fetch associations', [
'from_object' => $fromObject,
'to_object' => $toObject,
'reason' => $e->getMessage(),
]);
}
}
return $associationData;
}
/**
* @throws \Exception
*/
private function getNoteAssociationType(NoteObject $noteObject): string
{
return match($noteObject) {
NoteObject::Opportunity => 'note_to_deal',
NoteObject::Lead, NoteObject::Contact => 'note_to_contact', // or 'note_to_lead' if your portal supports it
NoteObject::Account => 'note_to_company',
NoteObject::Call, NoteObject::Event => throw new \Exception('Not supported'),
};
}
/**
* @throws \Exception
*/
private function getNoteObject(NoteObject $noteObject): string
{
return match($noteObject) {
NoteObject::Opportunity => 'deal',
NoteObject::Lead, NoteObject::Contact => 'contact',
NoteObject::Account => 'company',
NoteObject::Call, NoteObject::Event => throw new \Exception('Not supported'),
};
}
public function addAssociations(string $objectType, string $associationType, array $payload): Response
{
$endpoint = "/crm/v4/associations/$objectType/$associationType/batch/create";
return $this->makeRequest($endpoint, 'POST', $payload);
}
public function removeAssociations(string $objectType, string $associationType, array $payload): Response
{
$endpoint = "/crm/v4/associations/$objectType/$associationType/batch/archive";
return $this->makeRequest($endpoint, 'POST', $payload);
}
}
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