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18
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2026-05-07T11:22:45.959933+00:00
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/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778152965959_m1.jpg...
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iTerm2
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True
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monitor_1
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iTerm2ShellEditViewSessionScriptsProfilesWindowHel iTerm2ShellEditViewSessionScriptsProfilesWindowHelp(ah]Support Daily • in 38 m100% [Thu 7 May 14:22:46DEV (docker)*3DOCKERLast login: Thu MayO ₴1DEV (docker)7 09:44:56 on ttys006H82APP (-zsh)-zsh• 84screenpipe*•$5-zsh₴6Poetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentsPoetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentslukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-20773-fix-automated-reports-user-pilot-tracking) $ devroot@docker_lamp_1:/home/jiminny# php artisan crm:sync-opportunity --teamId=2 --from='2026-05-01 00:00:00*Syncing opportunity for HubspotYour HubSpotaccount has become disconnected. Please login to Jiminny to reconnect. skipping...root@docker_lamp_1:/home/jiminny# php artisan jiminny:token-info -A 1499 -RDEVaccess_tokenCFPAsBNZxoDp5kAcRyeB1QoE5SM7DSgNuYTFSAFoAYABo3tj9DHAAeAACNeR-JHgMxIZQLNQMl8kQEwrAgwACAkUAhIJBB4BAQEDBxiCiYwCIN7Y_Qwo0qwCMhTnG549n-YtNuc1jgj-2AsLPSmw3DoyQLNQM18kQEwrAiUACBkGawEFThwBARIBAQEEATEEAQEBAQEBAQEBAQUBEggBAQEBAY1access_token_expires_at= 2026-05-07 11:41:20refresh_token => d5ab04e2-2109-4c0b-b513-8cba1dd54371refresh_token_expires_at =root@docker_lamp_1:/home/jiminny#...
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-4995613387896938154
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click
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iTerm2ShellEditViewSessionScriptsProfilesWindowHel iTerm2ShellEditViewSessionScriptsProfilesWindowHelp(ah]Support Daily • in 38 m100% [Thu 7 May 14:22:46DEV (docker)*3DOCKERLast login: Thu MayO ₴1DEV (docker)7 09:44:56 on ttys006H82APP (-zsh)-zsh• 84screenpipe*•$5-zsh₴6Poetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentsPoetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentslukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-20773-fix-automated-reports-user-pilot-tracking) $ devroot@docker_lamp_1:/home/jiminny# php artisan crm:sync-opportunity --teamId=2 --from='2026-05-01 00:00:00*Syncing opportunity for HubspotYour HubSpotaccount has become disconnected. Please login to Jiminny to reconnect. skipping...root@docker_lamp_1:/home/jiminny# php artisan jiminny:token-info -A 1499 -RDEVaccess_tokenCFPAsBNZxoDp5kAcRyeB1QoE5SM7DSgNuYTFSAFoAYABo3tj9DHAAeAACNeR-JHgMxIZQLNQMl8kQEwrAgwACAkUAhIJBB4BAQEDBxiCiYwCIN7Y_Qwo0qwCMhTnG549n-YtNuc1jgj-2AsLPSmw3DoyQLNQM18kQEwrAiUACBkGawEFThwBARIBAQEEATEEAQEBAQEBAQEBAQUBEggBAQEBAY1access_token_expires_at= 2026-05-07 11:41:20refresh_token => d5ab04e2-2109-4c0b-b513-8cba1dd54371refresh_token_expires_at =root@docker_lamp_1:/home/jiminny#...
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106
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29
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2026-05-07T11:22:46.053941+00:00
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/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778152966053_m2.jpg...
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iTerm2
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NULL
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True
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monitor_2
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NULL
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NULL
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PostmanFileEditVIeWWindowmelpHubSpot rate limit i PostmanFileEditVIeWWindowmelpHubSpot rate limit implementation strategythem), not in any endpoint. It's a documented cons• Daily usage broken out by app within a portal — the daily endpoint aggregates all piapps. You can ttell from the Art wnich app spent the buaget.• Per-app burst limit programmatically — only inferred from Max in headers from a clthat app madePostman recipe to rully pronle a portalThree calls. in order:1. GET /account-info/v3/details →grab portalld, timeZone , also note the x-HubSpot-RateLimit-* res Reply^ iers (this is vour burst profile).2. GET /account-info/v3/api-usage/daily/private-apps daily limit, current spelreserome3. (Optional) Trigger a 429 deliberatelv on a sandbox to confirm policvName shape.brisn't necessarv iust for inspectionThat gives you everything Hubspot will tell you about a specific portal's limits. Save the 1requests as a Postman collection with {{access token?? and {{portal id?? as collecivariables and vou can profile anv nortal in two clicks.I can see daily https:api.hubapi.com/account-info/v3/details what aborsearch and ourstDistinguished burst limits via headers fromBurst: visible, but in response headers, not the body. Any non-search call works.in Pactman cand.GET https://api.hubapi.com/account-info/v3/detailsAuthorization: Bearer {token?Then in the response panel click the Headerst J .ot Body). You'll see something like:Keep going in Claude CodeSwitch to Claude Code and let Claude work directiv in vour reno..Write a message…Opus 4.7 AdaptilPlatform Sprint 3 Q2SevenShores\HubspeService-Desk - Queu• Jy 20807 check varioa Sentry••Pull requests • jiminnyU Useroilot I Ask JiminJY-20773 fix user pilcaJiminnuo Search the CRM - Hub— New TabXx Hubspot vQ SearchYour 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 •HTTP https:pi.hubapi.com/account-info/v3/detailsnttps://api.nubapi.com/account-info/vs/details= DocsAuthorization • Headers 9 Body ScriptsSettinasAuth TypeTokenine autnorization neader will oe automaticallygenerated when you send the reauest. Learnmore about Bearer Token authorization.GET read ci{tokenl}GET httos://:suppont Dally • In 3omNo environmentv~ SaveCookies100% L24* AIVariables in requestG token› All VarlablesInu / May 14-22.40UparadeCNeR-JHaMxlZoiNd.v COLLECtIONs› CRM ObjectsCRM Owners> CRM Pipelines> DealsEngagements• D OLD ENGAGEMENTSGET list meetingspost coarch tackeGET read call> PosT search callsGET list callsPOST meetings scheduledGET get meetingPOST get link to task> POST Create Contact with Association> HubsnotJournal & webhoooks v4POSt Get tokenPOST create subscrintionGET Journal earliestGET Journal latestGET https:/ubspot-webhooks-journal-nal.s.GET next offsetPOST aet Token prodDEL DELETE SUBSCRIPTION PER PORTALGET DEAL WITH HISTORY PROPERTIES V3> OAuth› Properties> PESCAPCHV SEARCHIPOST search contact by phonePost search contact ov emailliPOST search meetingsPOST search notes> post Soarch calle v2POST Search related meetinas v3ENMIDANMENTS> SPFCS>FLOWS@ Connect Git = Concold# TermBody Cookiesistatuscontent-tvoecf-rayef-cache-statusstrict-transport-securityaccess-control-allow-credentialsserver-timingyacontent-tune.ontionsx-hubspot-correlation-idx-hubspot-ratelimit-interval-millisecondswwlwerrwollmteohhnay-hubsnot-ratelimit-secondlv-remainingreport-tocontent-encodind200 OK • 209 ms • 1.24 KB • (| .•Thu. 07 Mav 2026 11:15:08 GMTIapplication/ison:charset=utf-89f7fce1hd89dhda6.sopDYNAMICmax-age=31536000; includeSubDomains: preloadorigin, Accept-Encodingralsehcid:desc="019e0226-0dba-7a1a-853b-a97a211c97f2" cfr'desc="9f7fce1be11d3402-IAD"nosniff019e0226-0dba-7a1a-853b-a97a211c976210000f"endpoints":[("url":*httos:Wa.nel.cloudflare.comVreportVv4?s=%2B8ZxvcfkqBM4vc64itFbi.f"cuecoss fraction".0.01 "ronort tol."of_nolkimayaao".604900}cloudflareGlobals Vault Tools?000...
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4738175950057134189
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click
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ocr
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PostmanFileEditVIeWWindowmelpHubSpot rate limit i PostmanFileEditVIeWWindowmelpHubSpot rate limit implementation strategythem), not in any endpoint. It's a documented cons• Daily usage broken out by app within a portal — the daily endpoint aggregates all piapps. You can ttell from the Art wnich app spent the buaget.• Per-app burst limit programmatically — only inferred from Max in headers from a clthat app madePostman recipe to rully pronle a portalThree calls. in order:1. GET /account-info/v3/details →grab portalld, timeZone , also note the x-HubSpot-RateLimit-* res Reply^ iers (this is vour burst profile).2. GET /account-info/v3/api-usage/daily/private-apps daily limit, current spelreserome3. (Optional) Trigger a 429 deliberatelv on a sandbox to confirm policvName shape.brisn't necessarv iust for inspectionThat gives you everything Hubspot will tell you about a specific portal's limits. Save the 1requests as a Postman collection with {{access token?? and {{portal id?? as collecivariables and vou can profile anv nortal in two clicks.I can see daily https:api.hubapi.com/account-info/v3/details what aborsearch and ourstDistinguished burst limits via headers fromBurst: visible, but in response headers, not the body. Any non-search call works.in Pactman cand.GET https://api.hubapi.com/account-info/v3/detailsAuthorization: Bearer {token?Then in the response panel click the Headerst J .ot Body). You'll see something like:Keep going in Claude CodeSwitch to Claude Code and let Claude work directiv in vour reno..Write a message…Opus 4.7 AdaptilPlatform Sprint 3 Q2SevenShores\HubspeService-Desk - Queu• Jy 20807 check varioa Sentry••Pull requests • jiminnyU Useroilot I Ask JiminJY-20773 fix user pilcaJiminnuo Search the CRM - Hub— New TabXx Hubspot vQ SearchYour 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 •HTTP https:pi.hubapi.com/account-info/v3/detailsnttps://api.nubapi.com/account-info/vs/details= DocsAuthorization • Headers 9 Body ScriptsSettinasAuth TypeTokenine autnorization neader will oe automaticallygenerated when you send the reauest. Learnmore about Bearer Token authorization.GET read ci{tokenl}GET httos://:suppont Dally • In 3omNo environmentv~ SaveCookies100% L24* AIVariables in requestG token› All VarlablesInu / May 14-22.40UparadeCNeR-JHaMxlZoiNd.v COLLECtIONs› CRM ObjectsCRM Owners> CRM Pipelines> DealsEngagements• D OLD ENGAGEMENTSGET list meetingspost coarch tackeGET read call> PosT search callsGET list callsPOST meetings scheduledGET get meetingPOST get link to task> POST Create Contact with Association> HubsnotJournal & webhoooks v4POSt Get tokenPOST create subscrintionGET Journal earliestGET Journal latestGET https:/ubspot-webhooks-journal-nal.s.GET next offsetPOST aet Token prodDEL DELETE SUBSCRIPTION PER PORTALGET DEAL WITH HISTORY PROPERTIES V3> OAuth› Properties> PESCAPCHV SEARCHIPOST search contact by phonePost search contact ov emailliPOST search meetingsPOST search notes> post Soarch calle v2POST Search related meetinas v3ENMIDANMENTS> SPFCS>FLOWS@ Connect Git = Concold# TermBody Cookiesistatuscontent-tvoecf-rayef-cache-statusstrict-transport-securityaccess-control-allow-credentialsserver-timingyacontent-tune.ontionsx-hubspot-correlation-idx-hubspot-ratelimit-interval-millisecondswwlwerrwollmteohhnay-hubsnot-ratelimit-secondlv-remainingreport-tocontent-encodind200 OK • 209 ms • 1.24 KB • (| .•Thu. 07 Mav 2026 11:15:08 GMTIapplication/ison:charset=utf-89f7fce1hd89dhda6.sopDYNAMICmax-age=31536000; includeSubDomains: preloadorigin, Accept-Encodingralsehcid:desc="019e0226-0dba-7a1a-853b-a97a211c97f2" cfr'desc="9f7fce1be11d3402-IAD"nosniff019e0226-0dba-7a1a-853b-a97a211c976210000f"endpoints":[("url":*httos:Wa.nel.cloudflare.comVreportVv4?s=%2B8ZxvcfkqBM4vc64itFbi.f"cuecoss fraction".0.01 "ronort tol."of_nolkimayaao".604900}cloudflareGlobals Vault Tools?000...
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105
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19
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2026-05-07T11:22:51.810506+00:00
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/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778152971810_m1.jpg...
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iTerm2
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True
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monitor_1
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iTerm2ShellEditViewSessionScriptsProfilesWindowHel iTerm2ShellEditViewSessionScriptsProfilesWindowHelp(ah]Support Daily • in 38 m100%8DEV (docker)*3Thu 7 May 14:22:51181₴6DOCKERLast login: Thu MayO ₴1DEV (docker)7 09:44:56 on ttys006H82APP (-zsh)-zsh• 84screenpipe*•$5-zshPoetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentsPoetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentslukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-20773-fix-automated-reports-user-pilot-tracking) $ devroot@docker_lamp_1:/home/jiminny# php artisan crm:sync-opportunity --teamId=2 --from='2026-05-01 00:00:00*Syncing opportunity for HubspotYour HubSpotaccount has become disconnected. Please login to Jiminny to reconnect. skipping...root@docker_lamp_1:/home/jiminny# php artisan jiminny:token-info -A 1499 -RDEVaccess_tokenCFPAsBNZxoDp5kAcRyeB1QoE5SM7DSgNuYTFSAFoAYABo3tj9DHAAeAACNeR-JHgMxIZQLNQMl8kQEwrAgwACAkUAhIJBB4BAQEDBxiCiYwCIN7Y_Qwo0qwCMhTnG549n-YtNuc1jgj-2AsLPSmw3DoyQLNQM18kQEwrAiUACBkGawEFThwBARIBAQEEATEEAQEBAQEBAQEBAQUBEggBAQEBAY1access_token_expires_at= 2026-05-07 11:41:20refresh_token => d5ab04e2-2109-4c0b-b513-8cba1dd54371refresh_token_expires_at =root@docker_lamp_1:/home/jiminny#...
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5249186614110437465
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click
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ocr
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iTerm2ShellEditViewSessionScriptsProfilesWindowHel iTerm2ShellEditViewSessionScriptsProfilesWindowHelp(ah]Support Daily • in 38 m100%8DEV (docker)*3Thu 7 May 14:22:51181₴6DOCKERLast login: Thu MayO ₴1DEV (docker)7 09:44:56 on ttys006H82APP (-zsh)-zsh• 84screenpipe*•$5-zshPoetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentsPoetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentslukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-20773-fix-automated-reports-user-pilot-tracking) $ devroot@docker_lamp_1:/home/jiminny# php artisan crm:sync-opportunity --teamId=2 --from='2026-05-01 00:00:00*Syncing opportunity for HubspotYour HubSpotaccount has become disconnected. Please login to Jiminny to reconnect. skipping...root@docker_lamp_1:/home/jiminny# php artisan jiminny:token-info -A 1499 -RDEVaccess_tokenCFPAsBNZxoDp5kAcRyeB1QoE5SM7DSgNuYTFSAFoAYABo3tj9DHAAeAACNeR-JHgMxIZQLNQMl8kQEwrAgwACAkUAhIJBB4BAQEDBxiCiYwCIN7Y_Qwo0qwCMhTnG549n-YtNuc1jgj-2AsLPSmw3DoyQLNQM18kQEwrAiUACBkGawEFThwBARIBAQEEATEEAQEBAQEBAQEBAQUBEggBAQEBAY1access_token_expires_at= 2026-05-07 11:41:20refresh_token => d5ab04e2-2109-4c0b-b513-8cba1dd54371refresh_token_expires_at =root@docker_lamp_1:/home/jiminny#...
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106
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30
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2026-05-07T11:22:51.810507+00:00
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/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778152971810_m2.jpg...
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iTerm2
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NULL
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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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PostmanFileEditVIewWindowmelpHubSpot rate limit i PostmanFileEditVIewWindowmelpHubSpot rate limit implementation strategythem), not in any endpoint. It's a documented cons• Daily usage broken out by app within a portal — the daily endpoint aggregates all piapps. You can t tenl from the Art wnich app spent the buaget.• Per-app burst limit programmatically — only inferred from Max in headers from a clthat app madePostman recipe to rully pronle a portalThree calls. in order:1. GET /account-info/v3/details →grab portalId, timeZone , also note the x-HubSpot-RateLimit-* res Reply ^ iers (this is vour burst profile).2. GET /account-info/v3/api-usage/daily/private-apps daily limit, current spelreserome3. (Optional) Trigger a 429 deliberatelv on a sandbox to confirm policvName shape.brisn't necessarv iust for inspectionThat gives you everything Hubspot will tell you about a specific portal's limits. Save the 1requests as a Postman collection with {{access token?? and {{portal id?? as collecivariables and vou can profile anv nortal in two clicks.I can see daily https:api.hubapi.com/account-info/v3/details what aborsearch and ourstDistinguished burst limits via headers fromBurst: visible, but in response headers, not the body. Any non-search call works.in Pactman cand.GET https://api.hubapi.com/account-info/v3/detailsAuthorization: Bearer {token?Then in the response panel click the Headerst J .ot Body). You'll see something like:Keep going in Claude CodeSwitch to Claude Code and let Claude work directiv in vour reno..Write a message…Opus 4.7 Adaptil# Support Daily - in 38 mNo environmentvg SaveThu 7 May 14:22:51Platform Sprint 3 Q2SevenShores|Hubsp.Service-Desk - Queu• Jy 20807 check varioa Sentry••Pull requests • jiminnyU Useroilot I Ask JiminJY-20773 fix user pilc8Jiminnyo Search the CRM - Hub— New TabXx Hubspot vQ SearchYour 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 read ciHTTP https:pi.hubapi.com/account-info/v3/api-usaqe/daily/private-appshttps://api.hubapi.com/account-info/v3/api-usage/daily/private-apps= DocsAuthorization • Headers 9 Body ScriptsSettinasTokenine autnorization neader will oe automaticallygenerated when you send the reauest. Learnmore about Bearer Token authorization.{tokenl}GET httos://:100% L24* AIVariables in requestG token› All Varlablesv COLLECtIONs› CRM ObjectsCRM Owners> CRM Pipelines> DealsEngagements• D OLD ENGAGEMENTSGET list meetingspost coarch tackeGET read call> PosT search callsGET list callsPOST meetings scheduledGET get meetingPOST get link to task> POST Create Contact with Association> HubsnotJournal & webhoooks v4POSt Get tokenPOST create subscrintionGET Journal earliestGET Journal latestGET https:/ubspot-webhooks-journal-nal.s.GET next offsetPOST aet Token prodDEL DELETE SUBSCRIPTION PER PORTALGET DEAL WITH HISTORY PROPERTIES V3› OAuth> Properties> PESCAPCHV SEARCHIPOST search contact by phonePost search contact ov emailliPOST search meetingsPOST search notes> post Soarch calle v2POST Search related meetinas v3ENMIDANMENTS> SPFCS>FLOWS@ Connect Git = Concold# TermCNeR-JHaMxlZoiNd.CookiesBodyHeaders 20 lest Resultsstatuscontent-tvoecf-rayef-cache-statusstrict-transport-securityaccess-control-allow-credentialsserver-timingyacontent-tune.ontionsx-hubspot-correlation-idx-hubspot-ratelimit-interval-millisecondswwlwerrwollmteohhnay-hubsnot-ratelimit-secondlv-remainingcontent-encodind200 OK • 209 ms • 1.24 KB • (| .•Thu. 07 Mav 2026 11:15:08 GMTIapplication/ison:charset=utf-89f7fce1hd89dhda6.sopDYNAMICmax-age=31536000; includeSubDomains: preloadorigin, Accept-Encodingralsehcid:desc="019e0226-0dba-7a1a-853b-a97a211c97f2" cfr'desc="9f7fce1be11d3402-IAD"nosniff019e0226-0dba-7a1a-853b-a97a211c9762f"endpoints":[("url":*httos:Wa.nel.cloudflare.comVreportVv4?s=%2B8ZxvcfkqBM4vc64itFbi.f"cuecoss fraction".0.01 "ronort tol."of_nolkimayaao".604900}cloudflareGlobals Vault Tools?000...
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6789000337323749298
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NULL
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click
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ocr
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PostmanFileEditVIewWindowmelpHubSpot rate limit i PostmanFileEditVIewWindowmelpHubSpot rate limit implementation strategythem), not in any endpoint. It's a documented cons• Daily usage broken out by app within a portal — the daily endpoint aggregates all piapps. You can t tenl from the Art wnich app spent the buaget.• Per-app burst limit programmatically — only inferred from Max in headers from a clthat app madePostman recipe to rully pronle a portalThree calls. in order:1. GET /account-info/v3/details →grab portalId, timeZone , also note the x-HubSpot-RateLimit-* res Reply ^ iers (this is vour burst profile).2. GET /account-info/v3/api-usage/daily/private-apps daily limit, current spelreserome3. (Optional) Trigger a 429 deliberatelv on a sandbox to confirm policvName shape.brisn't necessarv iust for inspectionThat gives you everything Hubspot will tell you about a specific portal's limits. Save the 1requests as a Postman collection with {{access token?? and {{portal id?? as collecivariables and vou can profile anv nortal in two clicks.I can see daily https:api.hubapi.com/account-info/v3/details what aborsearch and ourstDistinguished burst limits via headers fromBurst: visible, but in response headers, not the body. Any non-search call works.in Pactman cand.GET https://api.hubapi.com/account-info/v3/detailsAuthorization: Bearer {token?Then in the response panel click the Headerst J .ot Body). You'll see something like:Keep going in Claude CodeSwitch to Claude Code and let Claude work directiv in vour reno..Write a message…Opus 4.7 Adaptil# Support Daily - in 38 mNo environmentvg SaveThu 7 May 14:22:51Platform Sprint 3 Q2SevenShores|Hubsp.Service-Desk - Queu• Jy 20807 check varioa Sentry••Pull requests • jiminnyU Useroilot I Ask JiminJY-20773 fix user pilc8Jiminnyo Search the CRM - Hub— New TabXx Hubspot vQ SearchYour 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 read ciHTTP https:pi.hubapi.com/account-info/v3/api-usaqe/daily/private-appshttps://api.hubapi.com/account-info/v3/api-usage/daily/private-apps= DocsAuthorization • Headers 9 Body ScriptsSettinasTokenine autnorization neader will oe automaticallygenerated when you send the reauest. Learnmore about Bearer Token authorization.{tokenl}GET httos://:100% L24* AIVariables in requestG token› All Varlablesv COLLECtIONs› CRM ObjectsCRM Owners> CRM Pipelines> DealsEngagements• D OLD ENGAGEMENTSGET list meetingspost coarch tackeGET read call> PosT search callsGET list callsPOST meetings scheduledGET get meetingPOST get link to task> POST Create Contact with Association> HubsnotJournal & webhoooks v4POSt Get tokenPOST create subscrintionGET Journal earliestGET Journal latestGET https:/ubspot-webhooks-journal-nal.s.GET next offsetPOST aet Token prodDEL DELETE SUBSCRIPTION PER PORTALGET DEAL WITH HISTORY PROPERTIES V3› OAuth> Properties> PESCAPCHV SEARCHIPOST search contact by phonePost search contact ov emailliPOST search meetingsPOST search notes> post Soarch calle v2POST Search related meetinas v3ENMIDANMENTS> SPFCS>FLOWS@ Connect Git = Concold# TermCNeR-JHaMxlZoiNd.CookiesBodyHeaders 20 lest Resultsstatuscontent-tvoecf-rayef-cache-statusstrict-transport-securityaccess-control-allow-credentialsserver-timingyacontent-tune.ontionsx-hubspot-correlation-idx-hubspot-ratelimit-interval-millisecondswwlwerrwollmteohhnay-hubsnot-ratelimit-secondlv-remainingcontent-encodind200 OK • 209 ms • 1.24 KB • (| .•Thu. 07 Mav 2026 11:15:08 GMTIapplication/ison:charset=utf-89f7fce1hd89dhda6.sopDYNAMICmax-age=31536000; includeSubDomains: preloadorigin, Accept-Encodingralsehcid:desc="019e0226-0dba-7a1a-853b-a97a211c97f2" cfr'desc="9f7fce1be11d3402-IAD"nosniff019e0226-0dba-7a1a-853b-a97a211c9762f"endpoints":[("url":*httos:Wa.nel.cloudflare.comVreportVv4?s=%2B8ZxvcfkqBM4vc64itFbi.f"cuecoss fraction".0.01 "ronort tol."of_nolkimayaao".604900}cloudflareGlobals Vault Tools?000...
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2500
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20
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2026-05-07T11:22:58.857074+00:00
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/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778152978857_m1.jpg...
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iTerm2
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iTerm2ShellEditViewSessionScriptsProfilesWindowHel iTerm2ShellEditViewSessionScriptsProfilesWindowHelp(ah]Support Daily • in 38 m100%8DEV (docker)*3Thu 7 May 14:22:58T81₴6DOCKERLast login: Thu MayO ₴1DEV (docker)7 09:44:56 on ttys006H82APP (-zsh)-zsh• 84screenpipe*•$5-zshPoetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentsPoetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentslukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-20773-fix-automated-reports-user-pilot-tracking) $ devroot@docker_lamp_1:/home/jiminny# php artisan crm:sync-opportunity --teamId=2 --from='2026-05-01 00:00:00*Syncing opportunity for HubspotYour HubSpotaccount has become disconnected. Please login to Jiminny to reconnect. skipping...root@docker_lamp_1:/home/jiminny# php artisan jiminny:token-info -A 1499 -RDEVaccess_tokenCFPAsBNZxoDp5kAcRyeB1QoE5SM7DSgNuYTFSAFoAYABo3tj9DHAAeAACNeR-JHgMxIZQLNQMl8kQEwrAgwACAkUAhIJBB4BAQEDBxiCiYwCIN7Y_Qwo0qwCMhTnG549n-YtNuc1jgj-2AsLPSmw3DoyQLNQM18kQEwrAiUACBkGawEFThwBARIBAQEEATEEAQEBAQEBAQEBAQUBEggBAQEBAY1access_token_expires_at= 2026-05-07 11:41:20refresh_token => d5ab04e2-2109-4c0b-b513-8cba1dd54371refresh_token_expires_at =root@docker_lamp_1:/home/jiminny#...
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iTerm2ShellEditViewSessionScriptsProfilesWindowHel iTerm2ShellEditViewSessionScriptsProfilesWindowHelp(ah]Support Daily • in 38 m100%8DEV (docker)*3Thu 7 May 14:22:58T81₴6DOCKERLast login: Thu MayO ₴1DEV (docker)7 09:44:56 on ttys006H82APP (-zsh)-zsh• 84screenpipe*•$5-zshPoetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentsPoetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentslukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-20773-fix-automated-reports-user-pilot-tracking) $ devroot@docker_lamp_1:/home/jiminny# php artisan crm:sync-opportunity --teamId=2 --from='2026-05-01 00:00:00*Syncing opportunity for HubspotYour HubSpotaccount has become disconnected. Please login to Jiminny to reconnect. skipping...root@docker_lamp_1:/home/jiminny# php artisan jiminny:token-info -A 1499 -RDEVaccess_tokenCFPAsBNZxoDp5kAcRyeB1QoE5SM7DSgNuYTFSAFoAYABo3tj9DHAAeAACNeR-JHgMxIZQLNQMl8kQEwrAgwACAkUAhIJBB4BAQEDBxiCiYwCIN7Y_Qwo0qwCMhTnG549n-YtNuc1jgj-2AsLPSmw3DoyQLNQM18kQEwrAiUACBkGawEFThwBARIBAQEEATEEAQEBAQEBAQEBAQUBEggBAQEBAY1access_token_expires_at= 2026-05-07 11:41:20refresh_token => d5ab04e2-2109-4c0b-b513-8cba1dd54371refresh_token_expires_at =root@docker_lamp_1:/home/jiminny#...
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2026-05-07T11:22:58.857059+00:00
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/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778152978857_m2.jpg...
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iTerm2
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monitor_2
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PostmanFileEditVIeWWindowmelpHubSpot rate limit i PostmanFileEditVIeWWindowmelpHubSpot rate limit implementation strategythem), not in any endpoint. It's a documented cons• Daily usage broken out by app within a portal — the daily endpoint aggregates all piapps. You can t tenl from the Art wnich app spent the buaget.• Per-app burst limit programmatically — only inferred from Max in headers from a clthat app madePostman recipe to rully pronle a portalThree calls. in order:1. GET /account-info/v3/details →grab portalId, timeZone , also note the x-HubSpot-RateLimit-* res Reply ^ iers (this is vour burst profile).2. GET /account-info/v3/api-usage/daily/private-apps daily limit, current spelreserome3. (Optional) Trigger a 429 deliberatelv on a sandbox to confirm policvName shape.brisn't necessarv iust for inspectionThat gives you everything Hubspot will tell you about a specific portal's limits. Save the trequests as a Postman collection with {{access token?? and {{portal id?? as collecivariables and vou can profle anv nortal in two clicks.I can see daily https:api.hubapi.com/account-info/v3/details what aborsearch and ourstDistinguished burst limits via headers fromBurst: visible, but in response headers, not the body. Any non-search call works.in Pactman cand.GET [URL_WITH_CREDENTIALS] fraction":0.01"renort to"."cf-ne|""max age".604800%AlmurtorGlobals Vault Tools?000...
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PostmanFileEditVIeWWindowmelpHubSpot rate limit i PostmanFileEditVIeWWindowmelpHubSpot rate limit implementation strategythem), not in any endpoint. It's a documented cons• Daily usage broken out by app within a portal — the daily endpoint aggregates all piapps. You can t tenl from the Art wnich app spent the buaget.• Per-app burst limit programmatically — only inferred from Max in headers from a clthat app madePostman recipe to rully pronle a portalThree calls. in order:1. GET /account-info/v3/details →grab portalId, timeZone , also note the x-HubSpot-RateLimit-* res Reply ^ iers (this is vour burst profile).2. GET /account-info/v3/api-usage/daily/private-apps daily limit, current spelreserome3. (Optional) Trigger a 429 deliberatelv on a sandbox to confirm policvName shape.brisn't necessarv iust for inspectionThat gives you everything Hubspot will tell you about a specific portal's limits. Save the trequests as a Postman collection with {{access token?? and {{portal id?? as collecivariables and vou can profle anv nortal in two clicks.I can see daily https:api.hubapi.com/account-info/v3/details what aborsearch and ourstDistinguished burst limits via headers fromBurst: visible, but in response headers, not the body. Any non-search call works.in Pactman cand.GET [URL_WITH_CREDENTIALS] fraction":0.01"renort to"."cf-ne|""max age".604800%AlmurtorGlobals Vault Tools?000...
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2026-05-07T11:23:00.619964+00:00
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/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778152980619_m1.jpg...
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iTerm2
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True
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monitor_1
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iTerm2ShellEditViewSessionScriptsProfilesWindowHel iTerm2ShellEditViewSessionScriptsProfilesWindowHelp(ah]Support Daily • in 38 m100% [DEV (docker)*3Thu 7 May 14:23:00T81₴6DOCKERLast login: Thu MayO ₴1DEV (docker)7 09:44:56 on ttys006H82APP (-zsh)-zsh• 84screenpipe*•$5-zshPoetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentsPoetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentslukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-20773-fix-automated-reports-user-pilot-tracking) $ devroot@docker_lamp_1:/home/jiminny# php artisan crm:sync-opportunity --teamId=2 --from='2026-05-01 00:00:00*Syncing opportunity for HubspotYour HubSpotaccount has become disconnected. Please login to Jiminny to reconnect. skipping...root@docker_lamp_1:/home/jiminny# php artisan jiminny:token-info -A 1499 -RDEVaccess_tokenCFPAsBNZxoDp5kAcRyeB1QoE5SM7DSgNuYTFSAFoAYABo3tj9DHAAeAACNeR-JHgMxIZQLNQMl8kQEwrAgwACAkUAhIJBB4BAQEDBxiCiYwCIN7Y_Qwo0qwCMhTnG549n-YtNuc1jgj-2AsLPSmw3DoyQLNQM18kQEwrAiUACBkGawEFThwBARIBAQEEATEEAQEBAQEBAQEBAQUBEggBAQEBAY1access_token_expires_at= 2026-05-07 11:41:20refresh_token => d5ab04e2-2109-4c0b-b513-8cba1dd54371refresh_token_expires_at =root@docker_lamp_1:/home/jiminny#...
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558942383772893063
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iTerm2ShellEditViewSessionScriptsProfilesWindowHel iTerm2ShellEditViewSessionScriptsProfilesWindowHelp(ah]Support Daily • in 38 m100% [DEV (docker)*3Thu 7 May 14:23:00T81₴6DOCKERLast login: Thu MayO ₴1DEV (docker)7 09:44:56 on ttys006H82APP (-zsh)-zsh• 84screenpipe*•$5-zshPoetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentsPoetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentslukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-20773-fix-automated-reports-user-pilot-tracking) $ devroot@docker_lamp_1:/home/jiminny# php artisan crm:sync-opportunity --teamId=2 --from='2026-05-01 00:00:00*Syncing opportunity for HubspotYour HubSpotaccount has become disconnected. Please login to Jiminny to reconnect. skipping...root@docker_lamp_1:/home/jiminny# php artisan jiminny:token-info -A 1499 -RDEVaccess_tokenCFPAsBNZxoDp5kAcRyeB1QoE5SM7DSgNuYTFSAFoAYABo3tj9DHAAeAACNeR-JHgMxIZQLNQMl8kQEwrAgwACAkUAhIJBB4BAQEDBxiCiYwCIN7Y_Qwo0qwCMhTnG549n-YtNuc1jgj-2AsLPSmw3DoyQLNQM18kQEwrAiUACBkGawEFThwBARIBAQEEATEEAQEBAQEBAQEBAQUBEggBAQEBAY1access_token_expires_at= 2026-05-07 11:41:20refresh_token => d5ab04e2-2109-4c0b-b513-8cba1dd54371refresh_token_expires_at =root@docker_lamp_1:/home/jiminny#...
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2500
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2026-05-07T11:23:00.703996+00:00
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/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778152980703_m2.jpg...
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iTerm2
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NULL
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True
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monitor_2
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PostmanFileEditVIeWWindowmelpHubSpot rate limit i PostmanFileEditVIeWWindowmelpHubSpot rate limit implementation strategythem), not in any endpoint. It's a documented cons• Daily usage broken out by app within a portal — the daily endpoint aggregates all piapps. You can ttell from the Art wnich app spent the buaget.• Per-app burst limit programmatically — only inferred from Max in headers from a clthat app madePostman recipe to rully pronle a portalThree calls. in order:1. GET /account-info/v3/details →grab portalId, timeZone , also note the x-HubSpot-RateLimit-* res Reply ^ iers (this is vour burst profile).2. GET /account-info/v3/api-usage/daily/private-apps daily limit, current spelreserome3. (Optional) Trigger a 429 deliberatelv on a sandbox to confirm policvName shape.brisn't necessarv iust for inspectionThat gives you everything Hubspot will tell you about a specific portal's limits. Save the trequests as a Postman collection with {{access token?? and {{portal id?? as collecivariables and vou can profile anv nortal in two clicks.I can see daily https:api.hubapi.com/account-info/v3/details what aborsearch and ourstDistinguished burst limits via headers fromBurst: visible, but in response headers, not the body. Any non-search call works.in Pactman cand.GET [URL_WITH_CREDENTIALS] fraction":0.01"renort to"."cf-ne|""max age".604800%AlmudtoreGlobals Vault Tools?000...
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NULL
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click
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PostmanFileEditVIeWWindowmelpHubSpot rate limit i PostmanFileEditVIeWWindowmelpHubSpot rate limit implementation strategythem), not in any endpoint. It's a documented cons• Daily usage broken out by app within a portal — the daily endpoint aggregates all piapps. You can ttell from the Art wnich app spent the buaget.• Per-app burst limit programmatically — only inferred from Max in headers from a clthat app madePostman recipe to rully pronle a portalThree calls. in order:1. GET /account-info/v3/details →grab portalId, timeZone , also note the x-HubSpot-RateLimit-* res Reply ^ iers (this is vour burst profile).2. GET /account-info/v3/api-usage/daily/private-apps daily limit, current spelreserome3. (Optional) Trigger a 429 deliberatelv on a sandbox to confirm policvName shape.brisn't necessarv iust for inspectionThat gives you everything Hubspot will tell you about a specific portal's limits. Save the trequests as a Postman collection with {{access token?? and {{portal id?? as collecivariables and vou can profile anv nortal in two clicks.I can see daily https:api.hubapi.com/account-info/v3/details what aborsearch and ourstDistinguished burst limits via headers fromBurst: visible, but in response headers, not the body. Any non-search call works.in Pactman cand.GET [URL_WITH_CREDENTIALS] fraction":0.01"renort to"."cf-ne|""max age".604800%AlmudtoreGlobals Vault Tools?000...
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2504
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2026-05-07T11:23:15.221207+00:00
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/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778152995221_m1.jpg...
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iTerm2
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NULL
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True
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monitor_1
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NULL
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iTerm2ShellEditViewSessionScriptsProfilesWindowHel iTerm2ShellEditViewSessionScriptsProfilesWindowHelp(ahol§ Support Daily - in 37 m100%8DEV (docker)83Thu 7 May 14:23:15T81₴6DOCKERLast login: Thu MayDEV (docker)H82APP (-zsh)-zsh• 84screenpipe*•$5-zsh7 09:44:56 on ttys006Poetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentsPoetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentslukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-20773-fix-automated-reports-user-pilot-tracking) $ devroot@docker_lamp_1:/home/jiminny# php artisan crm:sync-opportunity --teamId=2 --from='2026-05-01 00:00:00*Syncing opportunity for HubspotYour HubSpotaccount has become disconnected. Please login to Jiminny to reconnect. skipping...root@docker_lamp_1:/home/jiminny# php artisan jiminny:token-info -A 1499 -RDEVaccess_tokenCFPAsBNZxoDp5kAcRyeB1QoE5SM7DSgNuYTFSAFoAYABo3tj9DHAAeAACNeR-JHgMxIZQLNQMl8kQEwrAgwACAkUAhIJBB4BAQEDBxiCiYwCIN7Y_Qwo0qwCMhTnG549n-YtNuc1jgj-2AsLPSmw3DoyQLNQM18kQEwrAiUACBkGawEFThwBARIBAQEEATEEAQEBAQEBAQEBAQUBEggBAQEBAY1access_token_expires_at= 2026-05-07 11:41:20refresh_token => d5ab04e2-2109-4c0b-b513-8cba1dd54371refresh_token_expires_at =root@docker_lamp_1:/home/jiminny#...
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NULL
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click
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ocr
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iTerm2ShellEditViewSessionScriptsProfilesWindowHel iTerm2ShellEditViewSessionScriptsProfilesWindowHelp(ahol§ Support Daily - in 37 m100%8DEV (docker)83Thu 7 May 14:23:15T81₴6DOCKERLast login: Thu MayDEV (docker)H82APP (-zsh)-zsh• 84screenpipe*•$5-zsh7 09:44:56 on ttys006Poetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentsPoetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentslukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-20773-fix-automated-reports-user-pilot-tracking) $ devroot@docker_lamp_1:/home/jiminny# php artisan crm:sync-opportunity --teamId=2 --from='2026-05-01 00:00:00*Syncing opportunity for HubspotYour HubSpotaccount has become disconnected. Please login to Jiminny to reconnect. skipping...root@docker_lamp_1:/home/jiminny# php artisan jiminny:token-info -A 1499 -RDEVaccess_tokenCFPAsBNZxoDp5kAcRyeB1QoE5SM7DSgNuYTFSAFoAYABo3tj9DHAAeAACNeR-JHgMxIZQLNQMl8kQEwrAgwACAkUAhIJBB4BAQEDBxiCiYwCIN7Y_Qwo0qwCMhTnG549n-YtNuc1jgj-2AsLPSmw3DoyQLNQM18kQEwrAiUACBkGawEFThwBARIBAQEEATEEAQEBAQEBAQEBAQUBEggBAQEBAY1access_token_expires_at= 2026-05-07 11:41:20refresh_token => d5ab04e2-2109-4c0b-b513-8cba1dd54371refresh_token_expires_at =root@docker_lamp_1:/home/jiminny#...
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2026-05-07T11:23:15.289669+00:00
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/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778152995289_m2.jpg...
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iTerm2
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monitor_2
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PostmanFileEditVIeWWindowmelpHubSpot rate limit i PostmanFileEditVIeWWindowmelpHubSpot rate limit implementation strategythem), not in any endpoint. It's a documented cons• Daily usage broken out by app within a portal — the daily endpoint aggregates all piapps. You can t tenl from the Art wnich app spent the buaget.• Per-app burst limit programmatically — only inferred from Max in headers from a clthat app madePostman recipe to rully pronle a portalThree calls. in order:1. GET /account-info/v3/details →grab portalId, timeZone , also note the x-HubSpot-RateLimit-* res Reply ^ iers (this is vour burst profile).2. GET /account-info/v3/api-usage/daily/private-apps daily limit, current spelreserome3. (Optional) Trigger a 429 deliberatelv on a sandbox to confirm policvName shape.brisn't necessarv iust for inspectionThat gives you everything Hubspot will tell you about a specific portal's limits. Save the trequests as a Postman collection with {{access token?? and {{portal id?? as collecivariables and vou can profile anv nortal in two clicks.I can see daily https:api.hubapi.com/account-info/v3/details what aborsearch and ourstDistinguished burst limits via headers fromBurst: visible, but in response headers, not the body. Any non-search call works.in Pactman cand.GET https://api.hubapi.com/account-info/v3/detailsAuthorization: Bearer {token?Then in the response panel click the Headerst J .ot Body). You'll see something like:Keep going in Claude CodeSwitch to Claude Code and let Claude work directiv in vour reno..Write a message…Opus 4.7 AdaptilThu 7 May 14:23:14Platform Sprint 3 Q2SevenShores\HubspeService-Desk - Queu• Jy 20807 check varioa Sentry••Pull requests • jiminnyU Useroilot I Ask JiminJY-20773 fix user pilc8Jiminnyo Search the CRM - Hub— New TabXx Hubspot vQ SearchYour 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 •HTTP https:pi.hubapi.com/account-info/v3/api-usaqe/daily/private-appshttps://api.hubapi.com/account-info/v3/api-usage/daily/private-appsE Docs Params Authorization • Headers 9 Body AScripts SettingsQuery ParamsKeyValueValueGET httos://:# Support Daily - in 37 mNo environmentv~ SaveCookiesBulk Edit ..100% L24* AIVariables in requestG token› All Varlablesv COLLECtIONs› CRM ObjectsCRM Owners> CRM Pipelines> DealsEngagements• D OLD ENGAGEMENTSGET list meetingspost coarch tackeGET read call> PosT search callsGET list callsPOST meetings scheduledGET get meetingPOST get link to task> POST Create Contact with Association> HubsnotJournal & webhoooks v4POSt Get tokenPOST create subscrintionGET Journal earliestGET Journal latestGET https:/ubspot-webhooks-journal-nal.s.GET next offsetPOST aet Token prodDEL DELETE SURSCRIPTION DER PORTALGET DEAL WITH HISTORY PROPERTIES V3> OAuth> Properties> PESCAPCHV SEARCHIPOST search contact by phonePost search contact ov emailliPOST search meetingsPOST search notes> post Soarch calle v2POST Search related meetinas v3post coarch doalsENMIDANMENTS> SPFCS>FLOWSConnect Git E Console 2 TermCNeR-JHaMxlZoiNd.DescriotionBodyHeaders 20 lest Resultsistatuscontent-tvoecf-rayef-cache-statusstrict-transport-securityaccess-control-allow-credentialsserver-timingyacontent-tune.ontionsx-hubspot-correlation-idx-hubspot-ratelimit-interval-millisecondswwlwerrwollmteohhnay-hubsnot-ratelimit-secondlv-remainingservecontent-encodind200 OK • 190 ms • 1.2 KB •C| .•Thu. 07 May 2026 11:23:01 GMTIapplication/ison:charset=utf-89f7fd9a4cfcA2d79-SoFDYNAMICmax-age=31536000: includeSubDomains: preloadorigin, Accept-Encodingralsehaid.daea-"0100022d.424h.2122.0222.179An6dd9780" Afridocn-"06764004d9602402-160"nosniff019e022d-434b-71c3-922a-178cafdd878e108f"endpoints":[("url":*httos:Wa.nel.cloudflare.comVreportVv4?s=qhtousin4YfDIMG27vDtmAf.f"cuecoss fraction".0.01 "ronort to"."af_nelkimay aao".604900}cloudflareGlobals Vault Tools?000...
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PostmanFileEditVIeWWindowmelpHubSpot rate limit i PostmanFileEditVIeWWindowmelpHubSpot rate limit implementation strategythem), not in any endpoint. It's a documented cons• Daily usage broken out by app within a portal — the daily endpoint aggregates all piapps. You can t tenl from the Art wnich app spent the buaget.• Per-app burst limit programmatically — only inferred from Max in headers from a clthat app madePostman recipe to rully pronle a portalThree calls. in order:1. GET /account-info/v3/details →grab portalId, timeZone , also note the x-HubSpot-RateLimit-* res Reply ^ iers (this is vour burst profile).2. GET /account-info/v3/api-usage/daily/private-apps daily limit, current spelreserome3. (Optional) Trigger a 429 deliberatelv on a sandbox to confirm policvName shape.brisn't necessarv iust for inspectionThat gives you everything Hubspot will tell you about a specific portal's limits. Save the trequests as a Postman collection with {{access token?? and {{portal id?? as collecivariables and vou can profile anv nortal in two clicks.I can see daily https:api.hubapi.com/account-info/v3/details what aborsearch and ourstDistinguished burst limits via headers fromBurst: visible, but in response headers, not the body. Any non-search call works.in Pactman cand.GET https://api.hubapi.com/account-info/v3/detailsAuthorization: Bearer {token?Then in the response panel click the Headerst J .ot Body). You'll see something like:Keep going in Claude CodeSwitch to Claude Code and let Claude work directiv in vour reno..Write a message…Opus 4.7 AdaptilThu 7 May 14:23:14Platform Sprint 3 Q2SevenShores\HubspeService-Desk - Queu• Jy 20807 check varioa Sentry••Pull requests • jiminnyU Useroilot I Ask JiminJY-20773 fix user pilc8Jiminnyo Search the CRM - Hub— New TabXx Hubspot vQ SearchYour 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 •HTTP https:pi.hubapi.com/account-info/v3/api-usaqe/daily/private-appshttps://api.hubapi.com/account-info/v3/api-usage/daily/private-appsE Docs Params Authorization • Headers 9 Body AScripts SettingsQuery ParamsKeyValueValueGET httos://:# Support Daily - in 37 mNo environmentv~ SaveCookiesBulk Edit ..100% L24* AIVariables in requestG token› All Varlablesv COLLECtIONs› CRM ObjectsCRM Owners> CRM Pipelines> DealsEngagements• D OLD ENGAGEMENTSGET list meetingspost coarch tackeGET read call> PosT search callsGET list callsPOST meetings scheduledGET get meetingPOST get link to task> POST Create Contact with Association> HubsnotJournal & webhoooks v4POSt Get tokenPOST create subscrintionGET Journal earliestGET Journal latestGET https:/ubspot-webhooks-journal-nal.s.GET next offsetPOST aet Token prodDEL DELETE SURSCRIPTION DER PORTALGET DEAL WITH HISTORY PROPERTIES V3> OAuth> Properties> PESCAPCHV SEARCHIPOST search contact by phonePost search contact ov emailliPOST search meetingsPOST search notes> post Soarch calle v2POST Search related meetinas v3post coarch doalsENMIDANMENTS> SPFCS>FLOWSConnect Git E Console 2 TermCNeR-JHaMxlZoiNd.DescriotionBodyHeaders 20 lest Resultsistatuscontent-tvoecf-rayef-cache-statusstrict-transport-securityaccess-control-allow-credentialsserver-timingyacontent-tune.ontionsx-hubspot-correlation-idx-hubspot-ratelimit-interval-millisecondswwlwerrwollmteohhnay-hubsnot-ratelimit-secondlv-remainingservecontent-encodind200 OK • 190 ms • 1.2 KB •C| .•Thu. 07 May 2026 11:23:01 GMTIapplication/ison:charset=utf-89f7fd9a4cfcA2d79-SoFDYNAMICmax-age=31536000: includeSubDomains: preloadorigin, Accept-Encodingralsehaid.daea-"0100022d.424h.2122.0222.179An6dd9780" Afridocn-"06764004d9602402-160"nosniff019e022d-434b-71c3-922a-178cafdd878e108f"endpoints":[("url":*httos:Wa.nel.cloudflare.comVreportVv4?s=qhtousin4YfDIMG27vDtmAf.f"cuecoss fraction".0.01 "ronort to"."af_nelkimay aao".604900}cloudflareGlobals Vault Tools?000...
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2026-05-07T11:23:16.051222+00:00
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/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778152996051_m1.jpg...
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iTerm2
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iTerm2ShellEditViewSessionScriptsProfilesWindowHel iTerm2ShellEditViewSessionScriptsProfilesWindowHelp(ahol§ Support Daily - in 37 m100%8DEV (docker)83Thu 7 May 14:23:16T81₴6DOCKERLast login: Thu MayDEV (docker)H82APP (-zsh)-zsh• 84screenpipe*•$5-zsh7 09:44:56 on ttys006Poetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentsPoetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentslukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-20773-fix-automated-reports-user-pilot-tracking) $ devroot@docker_lamp_1:/home/jiminny# php artisan crm:sync-opportunity --teamId=2 --from='2026-05-01 00:00:00*Syncing opportunity for HubspotYour HubSpotaccount has become disconnected. Please login to Jiminny to reconnect. skipping...root@docker_lamp_1:/home/jiminny# php artisan jiminny:token-info -A 1499 -RDEVaccess_tokenCFPAsBNZxoDp5kAcRyeB1QoE5SM7DSgNuYTFSAFoAYABo3tj9DHAAeAACNeR-JHgMxIZQLNQMl8kQEwrAgwACAkUAhIJBB4BAQEDBxiCiYwCIN7Y_Qwo0qwCMhTnG549n-YtNuc1jgj-2AsLPSmw3DoyQLNQM18kQEwrAiUACBkGawEFThwBARIBAQEEATEEAQEBAQEBAQEBAQUBEggBAQEBAY1access_token_expires_at= 2026-05-07 11:41:20refresh_token => d5ab04e2-2109-4c0b-b513-8cba1dd54371refresh_token_expires_at =root@docker_lamp_1:/home/jiminny#...
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iTerm2ShellEditViewSessionScriptsProfilesWindowHel iTerm2ShellEditViewSessionScriptsProfilesWindowHelp(ahol§ Support Daily - in 37 m100%8DEV (docker)83Thu 7 May 14:23:16T81₴6DOCKERLast login: Thu MayDEV (docker)H82APP (-zsh)-zsh• 84screenpipe*•$5-zsh7 09:44:56 on ttys006Poetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentsPoetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentslukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-20773-fix-automated-reports-user-pilot-tracking) $ devroot@docker_lamp_1:/home/jiminny# php artisan crm:sync-opportunity --teamId=2 --from='2026-05-01 00:00:00*Syncing opportunity for HubspotYour HubSpotaccount has become disconnected. Please login to Jiminny to reconnect. skipping...root@docker_lamp_1:/home/jiminny# php artisan jiminny:token-info -A 1499 -RDEVaccess_tokenCFPAsBNZxoDp5kAcRyeB1QoE5SM7DSgNuYTFSAFoAYABo3tj9DHAAeAACNeR-JHgMxIZQLNQMl8kQEwrAgwACAkUAhIJBB4BAQEDBxiCiYwCIN7Y_Qwo0qwCMhTnG549n-YtNuc1jgj-2AsLPSmw3DoyQLNQM18kQEwrAiUACBkGawEFThwBARIBAQEEATEEAQEBAQEBAQEBAQUBEggBAQEBAY1access_token_expires_at= 2026-05-07 11:41:20refresh_token => d5ab04e2-2109-4c0b-b513-8cba1dd54371refresh_token_expires_at =root@docker_lamp_1:/home/jiminny#...
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2026-05-07T11:23:17.511063+00:00
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/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778152997511_m2.jpg...
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iTerm2
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monitor_2
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PostmanFileEditVIewWindowmelpHubSpot rate limit i PostmanFileEditVIewWindowmelpHubSpot rate limit implementation strategythem), not in any endpoint. It's a documented cons• Daily usage broken out by app within a portal — the daily endpoint aggregates all piapps. You can t tenl from the Art wnich app spent the buaget.• Per-app burst limit programmatically — only inferred from Max in headers from a clthat app madePostman recipe to rully pronle a portalThree calls. in order:1. GET /account-info/v3/details →grab portalId, timeZone , also note the x-HubSpot-RateLimit-* res Reply ^ iers (this is vour burst profile).2. GET /account-info/v3/api-usage/daily/private-apps daily limit, current spelreserome3. (Optional) Trigger a 429 deliberatelv on a sandbox to confirm policvName shape.brisn't necessarv iust for inspectionThat gives you everything Hubspot will tell you about a specific portal's limits. Save the trequests as a Postman collection with {{access token?? and {{portal id?? as collecivariables and vou can profile anv nortal in two clicks.I can see daily https:api.hubapi.com/account-info/v3/details what aborsearch and ourstDistinguished burst limits via headers fromBurst: visible, but in response headers, not the body. Any non-search call works.in Pactman cand.GET https://api.hubapi.com/account-info/v3/detailsAuthorization: Bearer {token?Then in the response panel click the Headerst J .ot Body). You'll see something like:Keep going in Claude CodeSwitch to Claude Code and let Claude work directiv in vour reno..Write a message…Opus 4.7 AdaptilPlatform Sprint 3 Q2SevenShores\HubspeService-Desk - Queu• Jy 20807 check varioa Sentry••Pull requests • jiminnyU Useroilot I Ask JiminJY-20773 fix user pilc8Jiminnyo Search the CRM - Hub— New TabXx Hubspot vQ SearchYour 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 •HTTP https:pi.hubapi.com/account-info/v3/api-usaqe/daily/private-appshttps://api.hubapi.com/account-info/v3/api-usage/daily/private-apps= DocsAuthorization • Headers 9 Body Scripts Settinas© 9 hiddenValueValueGET httos://:# Support Daily - in 37 mNo environmentv~ SaveCookiesBulk Edit Presets v100% L24* AIVariables in requestG token› All VarlablesThu 7 May 14:23:17UparadeCNeR-JHaMxlZoiNd.v COLLECtIONs› CRM ObjectsCRM Owners> CRM Pipelines> DealsEngagements• D OLD ENGAGEMENTSGET list meetingspost coarch tackeGET read call> PosT search callsGET list callsPOST meetings scheduledGET get meetingPOST get link to task> POST Create Contact with Association> HubsnotJournal & webhoooks v4POSt Get tokenPOST create subscrintionGET Journal earliestGET Journal latestGET https:/ubspot-webhooks-journal-nal.s.GET next offsetPOST aet Token prodDEL DELETE SURSCRIPTION DER PORTALGET DEAL WITH HISTORY PROPERTIES V3› OAuth› Properties> PESCAPCHV SEARCHIPOST search contact by phonePost search contact ov emailliPOST search meetingsPOST search notespost Soarch calle v2POST Search related meetinas v3ENMIDANMENTS> SPFCS>FLOWSConnect Git E Console 2 TermDescriotionBOSNHeaders 20 lest Resultscontent-tvoecf-rayef-cache-statusstrict-transport-securityaccess-control-allow-credentialsserver-timingyacontent-tune.ontionsx-hubspot-correlation-idx-hubspot-ratelimit-interval-millisecondswwlwerrwollmteohhnay-hubsnot-ratelimit-secondlv-remaining200 OK • 190 ms • 1.2 KB •C| .•Thu. 07 May 2026 11:23:01 GMTIapplication/ison:charset=utf-89f7fd9a4cfcA2d79-SoFDYNAMICmax-age=31536000: includeSubDomains: preloadorigin, Accept-Encodingralsehaid.daea-"0100022d.424h.2122.0222.179An6dd9780" Afridocn-"06764004d9602402-160"nosniff019e022d-434b-71c3-922a-178cafdd878e108f"endpoints":[("url":*httos:Wa.nel.cloudflare.comVreportVv4?s=qhtousin4YfDIMG27vDtmAf.f"cuecoss fraction".0.01 "ronort to"."af_nelkimay aao".604900}cloudflarecontent-encodindGlobals Vault Tools?000...
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2945955814782390426
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click
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ocr
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PostmanFileEditVIewWindowmelpHubSpot rate limit i PostmanFileEditVIewWindowmelpHubSpot rate limit implementation strategythem), not in any endpoint. It's a documented cons• Daily usage broken out by app within a portal — the daily endpoint aggregates all piapps. You can t tenl from the Art wnich app spent the buaget.• Per-app burst limit programmatically — only inferred from Max in headers from a clthat app madePostman recipe to rully pronle a portalThree calls. in order:1. GET /account-info/v3/details →grab portalId, timeZone , also note the x-HubSpot-RateLimit-* res Reply ^ iers (this is vour burst profile).2. GET /account-info/v3/api-usage/daily/private-apps daily limit, current spelreserome3. (Optional) Trigger a 429 deliberatelv on a sandbox to confirm policvName shape.brisn't necessarv iust for inspectionThat gives you everything Hubspot will tell you about a specific portal's limits. Save the trequests as a Postman collection with {{access token?? and {{portal id?? as collecivariables and vou can profile anv nortal in two clicks.I can see daily https:api.hubapi.com/account-info/v3/details what aborsearch and ourstDistinguished burst limits via headers fromBurst: visible, but in response headers, not the body. Any non-search call works.in Pactman cand.GET https://api.hubapi.com/account-info/v3/detailsAuthorization: Bearer {token?Then in the response panel click the Headerst J .ot Body). You'll see something like:Keep going in Claude CodeSwitch to Claude Code and let Claude work directiv in vour reno..Write a message…Opus 4.7 AdaptilPlatform Sprint 3 Q2SevenShores\HubspeService-Desk - Queu• Jy 20807 check varioa Sentry••Pull requests • jiminnyU Useroilot I Ask JiminJY-20773 fix user pilc8Jiminnyo Search the CRM - Hub— New TabXx Hubspot vQ SearchYour 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 •HTTP https:pi.hubapi.com/account-info/v3/api-usaqe/daily/private-appshttps://api.hubapi.com/account-info/v3/api-usage/daily/private-apps= DocsAuthorization • Headers 9 Body Scripts Settinas© 9 hiddenValueValueGET httos://:# Support Daily - in 37 mNo environmentv~ SaveCookiesBulk Edit Presets v100% L24* AIVariables in requestG token› All VarlablesThu 7 May 14:23:17UparadeCNeR-JHaMxlZoiNd.v COLLECtIONs› CRM ObjectsCRM Owners> CRM Pipelines> DealsEngagements• D OLD ENGAGEMENTSGET list meetingspost coarch tackeGET read call> PosT search callsGET list callsPOST meetings scheduledGET get meetingPOST get link to task> POST Create Contact with Association> HubsnotJournal & webhoooks v4POSt Get tokenPOST create subscrintionGET Journal earliestGET Journal latestGET https:/ubspot-webhooks-journal-nal.s.GET next offsetPOST aet Token prodDEL DELETE SURSCRIPTION DER PORTALGET DEAL WITH HISTORY PROPERTIES V3› OAuth› Properties> PESCAPCHV SEARCHIPOST search contact by phonePost search contact ov emailliPOST search meetingsPOST search notespost Soarch calle v2POST Search related meetinas v3ENMIDANMENTS> SPFCS>FLOWSConnect Git E Console 2 TermDescriotionBOSNHeaders 20 lest Resultscontent-tvoecf-rayef-cache-statusstrict-transport-securityaccess-control-allow-credentialsserver-timingyacontent-tune.ontionsx-hubspot-correlation-idx-hubspot-ratelimit-interval-millisecondswwlwerrwollmteohhnay-hubsnot-ratelimit-secondlv-remaining200 OK • 190 ms • 1.2 KB •C| .•Thu. 07 May 2026 11:23:01 GMTIapplication/ison:charset=utf-89f7fd9a4cfcA2d79-SoFDYNAMICmax-age=31536000: includeSubDomains: preloadorigin, Accept-Encodingralsehaid.daea-"0100022d.424h.2122.0222.179An6dd9780" Afridocn-"06764004d9602402-160"nosniff019e022d-434b-71c3-922a-178cafdd878e108f"endpoints":[("url":*httos:Wa.nel.cloudflare.comVreportVv4?s=qhtousin4YfDIMG27vDtmAf.f"cuecoss fraction".0.01 "ronort to"."af_nelkimay aao".604900}cloudflarecontent-encodindGlobals Vault Tools?000...
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105
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24
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2026-05-07T11:23:17.585027+00:00
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/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778152997585_m1.jpg...
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iTerm2
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True
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iTerm2ShellEditViewSessionScriptsProfilesWindowHel iTerm2ShellEditViewSessionScriptsProfilesWindowHelp(aholSupport Daily • in 37 m100% [DEV (docker)83Thu 7 May 14:23:17181₴6DOCKERLast login: Thu MayO ₴1DEV (docker)7 09:44:56 on ttys006H82APP (-zsh)-zsh• 84screenpipe*•$5-zshPoetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentsPoetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentslukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-20773-fix-automated-reports-user-pilot-tracking) $ devroot@docker_lamp_1:/home/jiminny# php artisan crm:sync-opportunity --teamId=2 --from='2026-05-01 00:00:00*Syncing opportunity for HubspotYour HubSpotaccount has become disconnected. Please login to Jiminny to reconnect. skipping...root@docker_lamp_1:/home/jiminny# php artisan jiminny:token-info -A 1499 -RDEVaccess_tokenCFPAsBNZxoDp5kAcRyeB1QoE5SM7DSgNuYTFSAFoAYABo3tj9DHAAeAACNeR-JHgMxIZQLNQMl8kQEwrAgwACAkUAhIJBB4BAQEDBxiCiYwCIN7Y_Qwo0qwCMhTnG549n-YtNuc1jgj-2AsLPSmw3DoyQLNQM18kQEwrAiUACBkGawEFThwBARIBAQEEATEEAQEBAQEBAQEBAQUBEggBAQEBAY1access_token_expires_at= 2026-05-07 11:41:20refresh_token => d5ab04e2-2109-4c0b-b513-8cba1dd54371refresh_token_expires_at =root@docker_lamp_1:/home/jiminny#...
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NULL
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-7059590671337319883
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click
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ocr
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iTerm2ShellEditViewSessionScriptsProfilesWindowHel iTerm2ShellEditViewSessionScriptsProfilesWindowHelp(aholSupport Daily • in 37 m100% [DEV (docker)83Thu 7 May 14:23:17181₴6DOCKERLast login: Thu MayO ₴1DEV (docker)7 09:44:56 on ttys006H82APP (-zsh)-zsh• 84screenpipe*•$5-zshPoetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentsPoetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentslukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-20773-fix-automated-reports-user-pilot-tracking) $ devroot@docker_lamp_1:/home/jiminny# php artisan crm:sync-opportunity --teamId=2 --from='2026-05-01 00:00:00*Syncing opportunity for HubspotYour HubSpotaccount has become disconnected. Please login to Jiminny to reconnect. skipping...root@docker_lamp_1:/home/jiminny# php artisan jiminny:token-info -A 1499 -RDEVaccess_tokenCFPAsBNZxoDp5kAcRyeB1QoE5SM7DSgNuYTFSAFoAYABo3tj9DHAAeAACNeR-JHgMxIZQLNQMl8kQEwrAgwACAkUAhIJBB4BAQEDBxiCiYwCIN7Y_Qwo0qwCMhTnG549n-YtNuc1jgj-2AsLPSmw3DoyQLNQM18kQEwrAiUACBkGawEFThwBARIBAQEEATEEAQEBAQEBAQEBAQUBEggBAQEBAY1access_token_expires_at= 2026-05-07 11:41:20refresh_token => d5ab04e2-2109-4c0b-b513-8cba1dd54371refresh_token_expires_at =root@docker_lamp_1:/home/jiminny#...
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2509
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106
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35
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2026-05-07T11:23:18.617513+00:00
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/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778152998617_m2.jpg...
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iTerm2
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NULL
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True
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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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PostmanFileEditVIeWWindowmelpHubSpot rate limit i PostmanFileEditVIeWWindowmelpHubSpot rate limit implementation strategythem), not in any endpoint. It's a documented cons• Daily usage broken out by app within a portal — the daily endpoint aggregates all piapps. You can t tell from the Art wnich app spent the buaget.• Per-app burst limit programmatically — only inferred from Max in headers from a clthat app madePostman recipe to rully pronle a portalThree calls. in order:1. GET /account-info/v3/details →grab portalId, timeZone , also note the x-HubSpot-RateLimit-* res Reply^ iers (this is vour burst profile).2. GET /account-info/v3/api-usage/daily/private-apps daily limit, current spelreserome3. (Optional) Trigger a 429 deliberatelv on a sandbox to confirm policvName shape.brisn't necessarv iust for inspectionThat gives you everything Hubspot will tell you about a specific portal's limits. Save the 1requests as a Postman collection with {{access token?? and {{portal id?? as collecivariables and vou can profile anv nortal in two clicks.I can see daily https:api.hubapi.com/account-info/v3/details what aborsearch and ourstDistinguished burst limits via headers fromBurst: visible, but in response headers, not the body. Any non-search call works.in Pactman cand.GET https://api.hubapi.com/account-info/v3/detailsAuthorization: Bearer {token?Then in the response panel click the Headerst J .ot Body). You'll see something like:Keep going in Claude CodeSwitch to Claude Code and let Claude work directiv in vour reno..Write a message…Opus 4.7 Adaptil40 hiGET httos://:Thu 7 May 14:23:18Platform Sprint 3 Q2SevenShores|Hubsp.Service-Desk - Queu• Jy 20807 check varioa Sentry••Pull requests • jiminnyU Useroilot I Ask JiminJY-20773 fix user pilc8Jiminnyo Search the CRM - Hub— New TabXx Hubspot vQ SearchYour 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 •HTTP https:pi.hubapi.com/account-info/v3/api-usaqe/daily/private-appshttps://api.hubapi.com/account-info/v3/api-usage/daily/private-apps= DocsParams Authorization • Headers 9 Body Scripts SettinasHeaders© 9 hidden# Support Daily - in 37 mNo environmentg SaveCookiesBulk Edit Presetsv100% L24* AIVariables in requestG tokenAll variablesv COLLECtIONs> CRM ObjectsCRM Owners> CRM Pipelines> DealsEngagements• D OLD ENGAGEMENTSGET list meetingsPOST search moditied companiespost coarch tackeGET read call> PosT search callsGET list callsPOST meetings scheduledGET get meetingPOST get link to task> POST Create Contact with Association> HubsnotJournal & webhoooks v4POSt Get tokenGET get subscriptionsPOST create subscrintionGET Journal earliestGET Journal latestGET https:/ubspot-webhooks-journal-nal.s.GET neyt offsetPOST aet Token prodDEL DELETE SURSCRIPTION DER PORTAL.GET DEAL WITH HISTORY PROPERTIES V3> OAuth> Properties> PESCAPCHV SEARCHIPOST search contact by phonePost search contact ov emailliPOST search meetingsPOST search notes> post Soarch calle v2POST Search related meetinas v3post coarch doalsENMIDANMENTS> SPFCS>FLOWS@ Connect Git = Concole 5.) TerminCNeR-JHaMxlZoiNd.DescriotionBOСK""JSONvPreviewe. Visualize"results":"name": "private-aops-aoi-calls-dailv"."ucadel imi+". 1000000"collectedat": "2026-05-07T11:23:01.362Z""2026-05-08T04:00:002200 OK • 190 ms • 1.2 KB •C| .•=Q108Globals Vault Tools?000...
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6272796448807505190
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visual_change
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ocr
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PostmanFileEditVIeWWindowmelpHubSpot rate limit i PostmanFileEditVIeWWindowmelpHubSpot rate limit implementation strategythem), not in any endpoint. It's a documented cons• Daily usage broken out by app within a portal — the daily endpoint aggregates all piapps. You can t tell from the Art wnich app spent the buaget.• Per-app burst limit programmatically — only inferred from Max in headers from a clthat app madePostman recipe to rully pronle a portalThree calls. in order:1. GET /account-info/v3/details →grab portalId, timeZone , also note the x-HubSpot-RateLimit-* res Reply^ iers (this is vour burst profile).2. GET /account-info/v3/api-usage/daily/private-apps daily limit, current spelreserome3. (Optional) Trigger a 429 deliberatelv on a sandbox to confirm policvName shape.brisn't necessarv iust for inspectionThat gives you everything Hubspot will tell you about a specific portal's limits. Save the 1requests as a Postman collection with {{access token?? and {{portal id?? as collecivariables and vou can profile anv nortal in two clicks.I can see daily https:api.hubapi.com/account-info/v3/details what aborsearch and ourstDistinguished burst limits via headers fromBurst: visible, but in response headers, not the body. Any non-search call works.in Pactman cand.GET https://api.hubapi.com/account-info/v3/detailsAuthorization: Bearer {token?Then in the response panel click the Headerst J .ot Body). You'll see something like:Keep going in Claude CodeSwitch to Claude Code and let Claude work directiv in vour reno..Write a message…Opus 4.7 Adaptil40 hiGET httos://:Thu 7 May 14:23:18Platform Sprint 3 Q2SevenShores|Hubsp.Service-Desk - Queu• Jy 20807 check varioa Sentry••Pull requests • jiminnyU Useroilot I Ask JiminJY-20773 fix user pilc8Jiminnyo Search the CRM - Hub— New TabXx Hubspot vQ SearchYour 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 •HTTP https:pi.hubapi.com/account-info/v3/api-usaqe/daily/private-appshttps://api.hubapi.com/account-info/v3/api-usage/daily/private-apps= DocsParams Authorization • Headers 9 Body Scripts SettinasHeaders© 9 hidden# Support Daily - in 37 mNo environmentg SaveCookiesBulk Edit Presetsv100% L24* AIVariables in requestG tokenAll variablesv COLLECtIONs> CRM ObjectsCRM Owners> CRM Pipelines> DealsEngagements• D OLD ENGAGEMENTSGET list meetingsPOST search moditied companiespost coarch tackeGET read call> PosT search callsGET list callsPOST meetings scheduledGET get meetingPOST get link to task> POST Create Contact with Association> HubsnotJournal & webhoooks v4POSt Get tokenGET get subscriptionsPOST create subscrintionGET Journal earliestGET Journal latestGET https:/ubspot-webhooks-journal-nal.s.GET neyt offsetPOST aet Token prodDEL DELETE SURSCRIPTION DER PORTAL.GET DEAL WITH HISTORY PROPERTIES V3> OAuth> Properties> PESCAPCHV SEARCHIPOST search contact by phonePost search contact ov emailliPOST search meetingsPOST search notes> post Soarch calle v2POST Search related meetinas v3post coarch doalsENMIDANMENTS> SPFCS>FLOWS@ Connect Git = Concole 5.) TerminCNeR-JHaMxlZoiNd.DescriotionBOСK""JSONvPreviewe. Visualize"results":"name": "private-aops-aoi-calls-dailv"."ucadel imi+". 1000000"collectedat": "2026-05-07T11:23:01.362Z""2026-05-08T04:00:002200 OK • 190 ms • 1.2 KB •C| .•=Q108Globals Vault Tools?000...
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2026-05-07T11:23:20.328253+00:00
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iTerm2ShellEditViewSessionScriptsProfilesWindowHel iTerm2ShellEditViewSessionScriptsProfilesWindowHelp(aholSupport Daily • in 37 m100% [Thu 7 May 14:23:20DEV (docker)83DOCKERLast login: Thu MayO ₴1DEV (docker)7 09:44:56 on ttys006H82APP (-zsh)-zsh• 84screenpipe*•$5-zsh₴6Poetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentsPoetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentslukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-20773-fix-automated-reports-user-pilot-tracking) $ devroot@docker_lamp_1:/home/jiminny# php artisan crm:sync-opportunity --teamId=2 --from='2026-05-01 00:00:00*Syncing opportunity for HubspotYour HubSpotaccount has become disconnected. Please login to Jiminny to reconnect. skipping...root@docker_lamp_1:/home/jiminny# php artisan jiminny:token-info -A 1499 -RDEVaccess_tokenCFPAsBNZxoDp5kAcRyeB1QoE5SM7DSgNuYTFSAFoAYABo3tj9DHAAeAACNeR-JHgMxIZQLNQMl8kQEwrAgwACAkUAhIJBB4BAQEDBxiCiYwCIN7Y_Qwo0qwCMhTnG549n-YtNuc1jgj-2AsLPSmw3DoyQLNQM18kQEwrAiUACBkGawEFThwBARIBAQEEATEEAQEBAQEBAQEBAQUBEggBAQEBAY1access_token_expires_at= 2026-05-07 11:41:20refresh_token => d5ab04e2-2109-4c0b-b513-8cba1dd54371refresh_token_expires_at =root@docker_lamp_1:/home/jiminny#...
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iTerm2ShellEditViewSessionScriptsProfilesWindowHel iTerm2ShellEditViewSessionScriptsProfilesWindowHelp(aholSupport Daily • in 37 m100% [Thu 7 May 14:23:20DEV (docker)83DOCKERLast login: Thu MayO ₴1DEV (docker)7 09:44:56 on ttys006H82APP (-zsh)-zsh• 84screenpipe*•$5-zsh₴6Poetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentsPoetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentslukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-20773-fix-automated-reports-user-pilot-tracking) $ devroot@docker_lamp_1:/home/jiminny# php artisan crm:sync-opportunity --teamId=2 --from='2026-05-01 00:00:00*Syncing opportunity for HubspotYour HubSpotaccount has become disconnected. Please login to Jiminny to reconnect. skipping...root@docker_lamp_1:/home/jiminny# php artisan jiminny:token-info -A 1499 -RDEVaccess_tokenCFPAsBNZxoDp5kAcRyeB1QoE5SM7DSgNuYTFSAFoAYABo3tj9DHAAeAACNeR-JHgMxIZQLNQMl8kQEwrAgwACAkUAhIJBB4BAQEDBxiCiYwCIN7Y_Qwo0qwCMhTnG549n-YtNuc1jgj-2AsLPSmw3DoyQLNQM18kQEwrAiUACBkGawEFThwBARIBAQEEATEEAQEBAQEBAQEBAQUBEggBAQEBAY1access_token_expires_at= 2026-05-07 11:41:20refresh_token => d5ab04e2-2109-4c0b-b513-8cba1dd54371refresh_token_expires_at =root@docker_lamp_1:/home/jiminny#...
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PostmanFileEditVIeWWindowmelpHubSpot rate limit PostmanFileEditVIeWWindowmelpHubSpot rate limit implementation strategythem), not in any endpoint. It's a documented cons• Daily usage broken out by app within a portal — the daily endpoint aggregates all papps. You can t tell from the Art wnich app spent the buaget.• Per-app burst limit programmatically — only inferred from Max in headers from acthat app made.Postman recipe to fully profile a portalThree calls, in order:1. GET /account-info/v3/details → grab portalId, timeZone, also note the X-HubSpot-RateLimit-* res Reply * lers (this is your burst profile).2. GET /account-info/v3/api-usage/daily/private-apps → daily limit, current spereserome3. (Optional) Trigger a 429 deliberately on a sandbox to confirm policyName shape, biisn't necessary just for inspection.That gives you everything HubSpot will tell you about a specific portal's limits. Save the trequests as a Postman collection with ({{access_token}} and {{portal_idf} as collectvariables and you can profile any portal in two clicks.I can see daily [URL_WITH_CREDENTIALS] fix user pilo8JiminnyDổ Search the CRM - Hub— New TabXx Hubspot vQ SearchYour 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 •HTTP https:pi.hubapi.com/account-info/v3/api-usaqe/daily/private-appshttps://api.hubapi.com/account-info/v3/api-usage/daily/private-apps= DocsParams Authorization • Headers 9 Body Scripts SettinasHeaderso 9 hiddenSupport Daily - in 37 mNo environmentg SaveCookiesBulk Edit Presetsv100% L2* AIVariables in requestG tokenAll variablesv COLLECtIONs› CRM ObjectsCRM Owners› CRM Pipelines> DealsEngagements• D OLD ENGAGEMENTSGET list meetingsPOST search moditied companiesPost search tasksGET read call> Post search callsGET list callsPoST meetings scheduledGET get meetingPost get link to task> POST Create Contact with Association› HubspotJournal & webhoooks v4POST Get tokenGET get subscriptionsPOST create subscriptionGET Journal earliestGET Journal latestGET https:/ubspot-webhooks-journal-nal.s.GET next offsetPOST aet Token prodDEL DELETE SUBSCRIPTION PER PORTALGET DEAL WITH HISTORY PROPERTIES V3> OAuth› Properties> PESCAPCHV SEARCHIPoST search contact by phonePost search contact ov emailliPOST search meetingsPOST search notes> post Soarch calle v2POST Search related meetings v3PoSt search deals>ENVIRONMENTS> SPFCS>FLOWS$ Connect Git @ Console TermirCNeR-JHaMxlZoiNd.DescriotionBOdVCookiesJSONvPreviewe. Visualize"results":- nane"i mfrivatg-appo-api-calls-daily*,#currontllcado". Ol"collectedat": "2026-05-07T11:23:01.362Z""2026-05-08T04:00:002"200 OK • 190 ms • 1.2 KB •C| .•=Q108Globals Vault Tools?000...
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PostmanFileEditVIeWWindowmelpHubSpot rate limit PostmanFileEditVIeWWindowmelpHubSpot rate limit implementation strategythem), not in any endpoint. It's a documented cons• Daily usage broken out by app within a portal — the daily endpoint aggregates all papps. You can t tell from the Art wnich app spent the buaget.• Per-app burst limit programmatically — only inferred from Max in headers from acthat app made.Postman recipe to fully profile a portalThree calls, in order:1. GET /account-info/v3/details → grab portalId, timeZone, also note the X-HubSpot-RateLimit-* res Reply * lers (this is your burst profile).2. GET /account-info/v3/api-usage/daily/private-apps → daily limit, current spereserome3. (Optional) Trigger a 429 deliberately on a sandbox to confirm policyName shape, biisn't necessary just for inspection.That gives you everything HubSpot will tell you about a specific portal's limits. Save the trequests as a Postman collection with ({{access_token}} and {{portal_idf} as collectvariables and you can profile any portal in two clicks.I can see daily [URL_WITH_CREDENTIALS] fix user pilo8JiminnyDổ Search the CRM - Hub— New TabXx Hubspot vQ SearchYour 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 •HTTP https:pi.hubapi.com/account-info/v3/api-usaqe/daily/private-appshttps://api.hubapi.com/account-info/v3/api-usage/daily/private-apps= DocsParams Authorization • Headers 9 Body Scripts SettinasHeaderso 9 hiddenSupport Daily - in 37 mNo environmentg SaveCookiesBulk Edit Presetsv100% L2* AIVariables in requestG tokenAll variablesv COLLECtIONs› CRM ObjectsCRM Owners› CRM Pipelines> DealsEngagements• D OLD ENGAGEMENTSGET list meetingsPOST search moditied companiesPost search tasksGET read call> Post search callsGET list callsPoST meetings scheduledGET get meetingPost get link to task> POST Create Contact with Association› HubspotJournal & webhoooks v4POST Get tokenGET get subscriptionsPOST create subscriptionGET Journal earliestGET Journal latestGET https:/ubspot-webhooks-journal-nal.s.GET next offsetPOST aet Token prodDEL DELETE SUBSCRIPTION PER PORTALGET DEAL WITH HISTORY PROPERTIES V3> OAuth› Properties> PESCAPCHV SEARCHIPoST search contact by phonePost search contact ov emailliPOST search meetingsPOST search notes> post Soarch calle v2POST Search related meetings v3PoSt search deals>ENVIRONMENTS> SPFCS>FLOWS$ Connect Git @ Console TermirCNeR-JHaMxlZoiNd.DescriotionBOdVCookiesJSONvPreviewe. Visualize"results":- nane"i mfrivatg-appo-api-calls-daily*,#currontllcado". Ol"collectedat": "2026-05-07T11:23:01.362Z""2026-05-08T04:00:002"200 OK • 190 ms • 1.2 KB •C| .•=Q108Globals Vault Tools?000...
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2026-05-07T11:23:21.040748+00:00
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/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778153001040_m1.jpg...
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iTerm2ShellEditViewSessionScriptsProfilesWindowHel iTerm2ShellEditViewSessionScriptsProfilesWindowHelp(aholSupport Daily • in 37 m100% [DEV (docker)83Thu 7 May 14:23:21181₴6DOCKERLast login: Thu MayO ₴1DEV (docker)7 09:44:56 on ttys006H82APP (-zsh)-zsh• 84screenpipe*•$5-zshPoetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentsPoetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentslukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-20773-fix-automated-reports-user-pilot-tracking) $ devroot@docker_lamp_1:/home/jiminny# php artisan crm:sync-opportunity --teamId=2 --from='2026-05-01 00:00:00*Syncing opportunity for HubspotYour HubSpotaccount has become disconnected. Please login to Jiminny to reconnect. skipping...root@docker_lamp_1:/home/jiminny# php artisan jiminny:token-info -A 1499 -RDEVaccess_tokenCFPAsBNZxoDp5kAcRyeB1QoE5SM7DSgNuYTFSAFoAYABo3tj9DHAAeAACNeR-JHgMxIZQLNQMl8kQEwrAgwACAkUAhIJBB4BAQEDBxiCiYwCIN7Y_Qwo0qwCMhTnG549n-YtNuc1jgj-2AsLPSmw3DoyQLNQM18kQEwrAiUACBkGawEFThwBARIBAQEEATEEAQEBAQEBAQEBAQUBEggBAQEBAY1access_token_expires_at= 2026-05-07 11:41:20refresh_token => d5ab04e2-2109-4c0b-b513-8cba1dd54371refresh_token_expires_at =root@docker_lamp_1:/home/jiminny#...
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iTerm2ShellEditViewSessionScriptsProfilesWindowHel iTerm2ShellEditViewSessionScriptsProfilesWindowHelp(aholSupport Daily • in 37 m100% [DEV (docker)83Thu 7 May 14:23:21181₴6DOCKERLast login: Thu MayO ₴1DEV (docker)7 09:44:56 on ttys006H82APP (-zsh)-zsh• 84screenpipe*•$5-zshPoetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentsPoetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentslukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-20773-fix-automated-reports-user-pilot-tracking) $ devroot@docker_lamp_1:/home/jiminny# php artisan crm:sync-opportunity --teamId=2 --from='2026-05-01 00:00:00*Syncing opportunity for HubspotYour HubSpotaccount has become disconnected. Please login to Jiminny to reconnect. skipping...root@docker_lamp_1:/home/jiminny# php artisan jiminny:token-info -A 1499 -RDEVaccess_tokenCFPAsBNZxoDp5kAcRyeB1QoE5SM7DSgNuYTFSAFoAYABo3tj9DHAAeAACNeR-JHgMxIZQLNQMl8kQEwrAgwACAkUAhIJBB4BAQEDBxiCiYwCIN7Y_Qwo0qwCMhTnG549n-YtNuc1jgj-2AsLPSmw3DoyQLNQM18kQEwrAiUACBkGawEFThwBARIBAQEEATEEAQEBAQEBAQEBAQUBEggBAQEBAY1access_token_expires_at= 2026-05-07 11:41:20refresh_token => d5ab04e2-2109-4c0b-b513-8cba1dd54371refresh_token_expires_at =root@docker_lamp_1:/home/jiminny#...
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2026-05-07T11:23:23.612978+00:00
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/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778153003612_m1.jpg...
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iTerm2
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iTerm2ShellEditViewSessionScriptsProfilesWindowHel iTerm2ShellEditViewSessionScriptsProfilesWindowHelp(aholSupport Daily • in 37 m100% [Thu 7 May 14:23:23DEV (docker)83DOCKERLast login: Thu MayO ₴1DEV (docker)7 09:44:56 on ttys006H82APP (-zsh)-zsh• 84screenpipe*•$5-zsh₴6Poetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentsPoetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentslukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-20773-fix-automated-reports-user-pilot-tracking) $ devroot@docker_lamp_1:/home/jiminny# php artisan crm:sync-opportunity --teamId=2 --from='2026-05-01 00:00:00*Syncing opportunity for HubspotYour HubSpotaccount has become disconnected. Please login to Jiminny to reconnect. skipping...root@docker_lamp_1:/home/jiminny# php artisan jiminny:token-info -A 1499 -RDEVaccess_tokenCFPAsBNZxoDp5kAcRyeB1QoE5SM7DSgNuYTFSAFoAYABo3tj9DHAAeAACNeR-JHgMxIZQLNQMl8kQEwrAgwACAkUAhIJBB4BAQEDBxiCiYwCIN7Y_Qwo0qwCMhTnG549n-YtNuc1jgj-2AsLPSmw3DoyQLNQM18kQEwrAiUACBkGawEFThwBARIBAQEEATEEAQEBAQEBAQEBAQUBEggBAQEBAY1access_token_expires_at= 2026-05-07 11:41:20refresh_token => d5ab04e2-2109-4c0b-b513-8cba1dd54371refresh_token_expires_at =root@docker_lamp_1:/home/jiminny#...
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iTerm2ShellEditViewSessionScriptsProfilesWindowHel iTerm2ShellEditViewSessionScriptsProfilesWindowHelp(aholSupport Daily • in 37 m100% [Thu 7 May 14:23:23DEV (docker)83DOCKERLast login: Thu MayO ₴1DEV (docker)7 09:44:56 on ttys006H82APP (-zsh)-zsh• 84screenpipe*•$5-zsh₴6Poetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentsPoetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentslukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-20773-fix-automated-reports-user-pilot-tracking) $ devroot@docker_lamp_1:/home/jiminny# php artisan crm:sync-opportunity --teamId=2 --from='2026-05-01 00:00:00*Syncing opportunity for HubspotYour HubSpotaccount has become disconnected. Please login to Jiminny to reconnect. skipping...root@docker_lamp_1:/home/jiminny# php artisan jiminny:token-info -A 1499 -RDEVaccess_tokenCFPAsBNZxoDp5kAcRyeB1QoE5SM7DSgNuYTFSAFoAYABo3tj9DHAAeAACNeR-JHgMxIZQLNQMl8kQEwrAgwACAkUAhIJBB4BAQEDBxiCiYwCIN7Y_Qwo0qwCMhTnG549n-YtNuc1jgj-2AsLPSmw3DoyQLNQM18kQEwrAiUACBkGawEFThwBARIBAQEEATEEAQEBAQEBAQEBAQUBEggBAQEBAY1access_token_expires_at= 2026-05-07 11:41:20refresh_token => d5ab04e2-2109-4c0b-b513-8cba1dd54371refresh_token_expires_at =root@docker_lamp_1:/home/jiminny#...
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/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778153003612_m2.jpg...
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iTerm2
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monitor_2
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PostmanFileEditVIeWWindowmelpHubSpot rate limit i PostmanFileEditVIeWWindowmelpHubSpot rate limit implementation strategythem), not in any endpoint. It's a documented cons• Daily usage broken out by app within a portal — the daily endpoint aggregates all piapps. You can t tell from the Art wnich app spent the buaget.• Per-app burst limit programmatically — only inferred from Max in headers from a clthat app madePostman recipe to rully pronle a portalThree calls. in order:1. GET /account-info/v3/details →grab portalId, timeZone , also note the x-HubSpot-RateLimit-* res Reply^ iers (this is vour burst profile).2. GET /account-info/v3/api-usage/daily/private-apps daily limit, current spelreserome3. (Optional) Trigger a 429 deliberatelv on a sandbox to confirm policvName shape.brisn't necessarv iust for inspectionThat gives you everything Hubspot will tell you about a specific portal's limits. Save the 1requests as a Postman collection with {{access token?? and {{portal id?? as collecivariables and vou can profile anv nortal in two clicks.I can see daily https:api.hubapi.com/account-info/v3/details what aborsearch and ourstDistinguished burst limits via headers fromBurst: visible, but in response headers, not the body. Any non-search call works.n Pactman cond.GET https://api.hubapi.com/account-info/v3/detailsAuthorization: Bearer {token?Then in the response panel click the Headerst J .ot Body). You'll see something like:Keep going in Claude CodeSwitch to Claude Code and let Claude work directiv in vour reno..Write a message…Opus 4.7 Adaptil40 hiGET httos://:Thu 7 May 14:23:23Platform Sprint 3 Q2SevenShores\HubspeService-Desk - Queu• Jy 20807 check varioa Sentry••Pull requests • jiminnyU Useroilot I Ask JiminJY-20773 fix user pilc8Jiminnyo Search the CRM - HubXx Hubspot vQ SearchYour 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 •HTTP https:pi.hubapi.com/account-info/v3/api-usaqe/daily/private-appshttps://api.hubapi.com/account-info/v3/api-usage/daily/private-apps= DocsParams Authorization • Headers 9 Body Scripts Settinas© 9 hidden# Support Daily - in 37 mNo environmentvg SaveCookiesBulk Edit Presets v100% L24* AIVariables in requestG tokenAll variablesv COLLECtIONs› CRM ObjectsCRM Owners> CRM Pipelines> DealsEngagements• D OLD ENGAGEMENTSGET list meetingsPOST search moditied companiespost coarch tackeGET read call> PosT search callsGET list callsPOST meetings scheduledGET get meetingPOST get link to task> POST Create Contact with Association> HubsnotJournal & webhoooks v4POSt Get tokenGET get subscriptionsPOST create subscrintionGET Journal earliestGET Journal latestGET https:/ubspot-webhooks-journal-nal.s.GET neyt offsetPOST aet Token prodDEL DELETE SURSCRIPTION DER PORTAL.GET DEAL WITH HISTORY PROPERTIES V3> OAuth> Properties> PESCAPCHV SEARCHIPOST search contact by phonePost search contact ov emailliPOST search meetingsPOST search notespost Soarch calle v2POST Search related meetinas v3post coarch doalsENMIDANMENTS> SPFCS>FLOWS@ Connect Git = Concole 5.) TerminCNeR-JHaMxlZoiNd.DescriotionBOdVCookiesJSONvPreviewe. Visualize"results":"name": "private-apos-api-calls-dailly""usagelimit": 1000000,"collectedat": "2026-05-07T11:23:01.362Z""2026-05-08T04:00:002200 OK • 190 ms • 1.2 KB •C| .•Globals Vault Tools?000...
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PostmanFileEditVIeWWindowmelpHubSpot rate limit i PostmanFileEditVIeWWindowmelpHubSpot rate limit implementation strategythem), not in any endpoint. It's a documented cons• Daily usage broken out by app within a portal — the daily endpoint aggregates all piapps. You can t tell from the Art wnich app spent the buaget.• Per-app burst limit programmatically — only inferred from Max in headers from a clthat app madePostman recipe to rully pronle a portalThree calls. in order:1. GET /account-info/v3/details →grab portalId, timeZone , also note the x-HubSpot-RateLimit-* res Reply^ iers (this is vour burst profile).2. GET /account-info/v3/api-usage/daily/private-apps daily limit, current spelreserome3. (Optional) Trigger a 429 deliberatelv on a sandbox to confirm policvName shape.brisn't necessarv iust for inspectionThat gives you everything Hubspot will tell you about a specific portal's limits. Save the 1requests as a Postman collection with {{access token?? and {{portal id?? as collecivariables and vou can profile anv nortal in two clicks.I can see daily https:api.hubapi.com/account-info/v3/details what aborsearch and ourstDistinguished burst limits via headers fromBurst: visible, but in response headers, not the body. Any non-search call works.n Pactman cond.GET https://api.hubapi.com/account-info/v3/detailsAuthorization: Bearer {token?Then in the response panel click the Headerst J .ot Body). You'll see something like:Keep going in Claude CodeSwitch to Claude Code and let Claude work directiv in vour reno..Write a message…Opus 4.7 Adaptil40 hiGET httos://:Thu 7 May 14:23:23Platform Sprint 3 Q2SevenShores\HubspeService-Desk - Queu• Jy 20807 check varioa Sentry••Pull requests • jiminnyU Useroilot I Ask JiminJY-20773 fix user pilc8Jiminnyo Search the CRM - HubXx Hubspot vQ SearchYour 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 •HTTP https:pi.hubapi.com/account-info/v3/api-usaqe/daily/private-appshttps://api.hubapi.com/account-info/v3/api-usage/daily/private-apps= DocsParams Authorization • Headers 9 Body Scripts Settinas© 9 hidden# Support Daily - in 37 mNo environmentvg SaveCookiesBulk Edit Presets v100% L24* AIVariables in requestG tokenAll variablesv COLLECtIONs› CRM ObjectsCRM Owners> CRM Pipelines> DealsEngagements• D OLD ENGAGEMENTSGET list meetingsPOST search moditied companiespost coarch tackeGET read call> PosT search callsGET list callsPOST meetings scheduledGET get meetingPOST get link to task> POST Create Contact with Association> HubsnotJournal & webhoooks v4POSt Get tokenGET get subscriptionsPOST create subscrintionGET Journal earliestGET Journal latestGET https:/ubspot-webhooks-journal-nal.s.GET neyt offsetPOST aet Token prodDEL DELETE SURSCRIPTION DER PORTAL.GET DEAL WITH HISTORY PROPERTIES V3> OAuth> Properties> PESCAPCHV SEARCHIPOST search contact by phonePost search contact ov emailliPOST search meetingsPOST search notespost Soarch calle v2POST Search related meetinas v3post coarch doalsENMIDANMENTS> SPFCS>FLOWS@ Connect Git = Concole 5.) TerminCNeR-JHaMxlZoiNd.DescriotionBOdVCookiesJSONvPreviewe. Visualize"results":"name": "private-apos-api-calls-dailly""usagelimit": 1000000,"collectedat": "2026-05-07T11:23:01.362Z""2026-05-08T04:00:002200 OK • 190 ms • 1.2 KB •C| .•Globals Vault Tools?000...
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PostmanFileEditVIeWWindowmelpHubSpot rate limit PostmanFileEditVIeWWindowmelpHubSpot rate limit implementation strategythem), not in any endpoint. It's a documented cons• Daily usage broken out by app within a portal — the daily endpoint aggregates all papps. You can t tell from the Art wnich app spent the buaget.• Per-app burst limit programmatically — only inferred from Max in headers from acthat app made.Postman recipe to fully profile a portalThree calls, in order:1. GET /account-info/v3/details → grab portalId, timeZone, also note the X-HubSpot-RateLimit-* res Reply * lers (this is your burst profile).2. GET /account-info/v3/api-usage/daily/private-apps → daily limit, current spereserome3. (Optional) Trigger a 429 deliberately on a sandbox to confirm policyName shape, biisn't necessary just for inspection.That gives you everything HubSpot will tell you about a specific portal's limits. Save thetrequests as a Postman collection with ({{access_token}} and {{portal_id}} as collectvariables and you can profile any portal in two clicks.I can see daily [URL_WITH_CREDENTIALS] fix user piloaJiminnuDổ Search the CRM - Hub— New TabXx Hubspot vQ SearchYour 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 collaborationGET next off. • POST search •i https://api.hubapi.com/account-info/v3/api-usage/daily/private-appshttps://api.hubapi.com/account-info/v3/api-usage/daily/private-apps# DocsHeaderso 9 hiddenAuthorization • Headers 9 Body Scripts SettingsSO WOSupport Daily • in 37 mGET httos://:No environng SaveCookiesBulk Edit Presets100% C4)* AIVariables in requestG tokenAll variablesv COLLECtIONs› CRM ObjectsCRM Owners› CRM Pipelines> DealsEngagements• D OLD ENGAGEMENTSGET list meetingsPost search tasksGET read call> post search callsGET list callsPoST meetings scheduledGET get meetingPost get link to task> POST Create Contact with Association› HubspotJournal & webhoooks v4POST Get tokenPOST create subscriptionGET Journal earliestGET Journal latestGET httos:/ubspot-webhooks-journal-na1.sGET neyt offsetPOST aet Token prodDEL DELETE SUBSCRIPTION PER PORTALGET DEAL WITH HISTORY PROPERTIES V3> OAuth> Properties> PESCAPCHV SEARCHIPoST search contact by phonePost search contact ov emailliPOST search meetingsPOST search notes> post Soarch calle v2POST Search related meetinas v3>ENVIRONMENTS) spFcs>FLOWS@ Connect Git = Concole 5.) TermCNeR-JHaMxlZoiNO.DescriotionBOdVCookies 1# IB USON ~Previewe. Visualize200 OK • 190 ms • 1.2 KB •C| .•=aIDO"collectedAt" : "2026-05-07711:23:01,3622*.Globals Vault Toos S 0 00...
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PostmanFileEditVIeWWindowmelpHubSpot rate limit PostmanFileEditVIeWWindowmelpHubSpot rate limit implementation strategythem), not in any endpoint. It's a documented cons• Daily usage broken out by app within a portal — the daily endpoint aggregates all papps. You can t tell from the Art wnich app spent the buaget.• Per-app burst limit programmatically — only inferred from Max in headers from acthat app made.Postman recipe to fully profile a portalThree calls, in order:1. GET /account-info/v3/details → grab portalId, timeZone, also note the X-HubSpot-RateLimit-* res Reply * lers (this is your burst profile).2. GET /account-info/v3/api-usage/daily/private-apps → daily limit, current spereserome3. (Optional) Trigger a 429 deliberately on a sandbox to confirm policyName shape, biisn't necessary just for inspection.That gives you everything HubSpot will tell you about a specific portal's limits. Save thetrequests as a Postman collection with ({{access_token}} and {{portal_id}} as collectvariables and you can profile any portal in two clicks.I can see daily [URL_WITH_CREDENTIALS] fix user piloaJiminnuDổ Search the CRM - Hub— New TabXx Hubspot vQ SearchYour 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 collaborationGET next off. • POST search •i https://api.hubapi.com/account-info/v3/api-usage/daily/private-appshttps://api.hubapi.com/account-info/v3/api-usage/daily/private-apps# DocsHeaderso 9 hiddenAuthorization • Headers 9 Body Scripts SettingsSO WOSupport Daily • in 37 mGET httos://:No environng SaveCookiesBulk Edit Presets100% C4)* AIVariables in requestG tokenAll variablesv COLLECtIONs› CRM ObjectsCRM Owners› CRM Pipelines> DealsEngagements• D OLD ENGAGEMENTSGET list meetingsPost search tasksGET read call> post search callsGET list callsPoST meetings scheduledGET get meetingPost get link to task> POST Create Contact with Association› HubspotJournal & webhoooks v4POST Get tokenPOST create subscriptionGET Journal earliestGET Journal latestGET httos:/ubspot-webhooks-journal-na1.sGET neyt offsetPOST aet Token prodDEL DELETE SUBSCRIPTION PER PORTALGET DEAL WITH HISTORY PROPERTIES V3> OAuth> Properties> PESCAPCHV SEARCHIPoST search contact by phonePost search contact ov emailliPOST search meetingsPOST search notes> post Soarch calle v2POST Search related meetinas v3>ENVIRONMENTS) spFcs>FLOWS@ Connect Git = Concole 5.) TermCNeR-JHaMxlZoiNO.DescriotionBOdVCookies 1# IB USON ~Previewe. Visualize200 OK • 190 ms • 1.2 KB •C| .•=aIDO"collectedAt" : "2026-05-07711:23:01,3622*.Globals Vault Toos S 0 00...
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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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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
]...
|
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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":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":". 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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. 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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"}]...
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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
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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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
]...
|
NULL
|
NULL
|
NULL
|
NULL
|
|
2517
|
106
|
39
|
2026-05-07T11:23:25.851848+00:00
|
/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778153005851_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
(...
|
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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"},{"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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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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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
(...
|
2515
|
NULL
|
NULL
|
NULL
|
|
2518
|
106
|
40
|
2026-05-07T11:23:28.069229+00:00
|
/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778153008069_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
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 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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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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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":"). 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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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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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3462968482044140636
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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
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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
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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...
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2026-05-07T11:23:28.139289+00:00
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/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778153008139_m1.jpg...
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Claude
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Claude
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monitor_1
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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
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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.
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
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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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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:
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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...
|
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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). 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"},{"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"}]...
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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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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...
|
2516
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NULL
|
NULL
|
NULL
|
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2520
|
105
|
30
|
2026-05-07T11:23:30.089296+00:00
|
/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778153010089_m1.jpg...
|
Claude
|
Claude
|
True
|
NULL
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monitor_1
|
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
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:...
|
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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. 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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"}]...
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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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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
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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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
[...
|
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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":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). 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"},{"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"}]...
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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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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
]
)
local
burst_used
=
redis
.
call
(
'ZCARD'
,
KEYS
[...
|
2518
|
NULL
|
NULL
|
NULL
|
|
2522
|
105
|
31
|
2026-05-07T11:23:30.754345+00:00
|
/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778153010754_m1.jpg...
|
Claude
|
Claude
|
True
|
NULL
|
monitor_1
|
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
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
]
+...
|
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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":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). 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"},{"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"}]...
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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
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
]
+...
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iTerm2ShellEditViewSessionScriptsProfilesWindowHel iTerm2ShellEditViewSessionScriptsProfilesWindowHelp(aholSupport Daily • in 37 m100% [Thu 7 May 14:23:34DEV (docker)83DOCKERLast login: Thu MayO ₴1DEV (docker)7 09:44:56 on ttys006H82APP (-zsh)-zsh• 84screenpipe*•$5-zsh₴6Poetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentsPoetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentslukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-20773-fix-automated-reports-user-pilot-tracking) $ devroot@docker_lamp_1:/home/jiminny# php artisan crm:sync-opportunity --teamId=2 --from='2026-05-01 00:00:00*Syncing opportunity for HubspotYour HubSpotaccount has become disconnected. Please login to Jiminny to reconnect. skipping...root@docker_lamp_1:/home/jiminny# php artisan jiminny:token-info -A 1499 -RDEVaccess_tokenCFPAsBNZxoDp5kAcRyeB1QoE5SM7DSgNuYTFSAFoAYABo3tj9DHAAeAACNeR-JHgMxIZQLNQMl8kQEwrAgwACAkUAhIJBB4BAQEDBxiCiYwCIN7Y_Qwo0qwCMhTnG549n-YtNuc1jgj-2AsLPSmw3DoyQLNQM18kQEwrAiUACBkGawEFThwBARIBAQEEATEEAQEBAQEBAQEBAQUBEggBAQEBAY1access_token_expires_at= 2026-05-07 11:41:20refresh_token => d5ab04e2-2109-4c0b-b513-8cba1dd54371refresh_token_expires_at =root@docker_lamp_1:/home/jiminny#...
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iTerm2ShellEditViewSessionScriptsProfilesWindowHel iTerm2ShellEditViewSessionScriptsProfilesWindowHelp(aholSupport Daily • in 37 m100% [Thu 7 May 14:23:34DEV (docker)83DOCKERLast login: Thu MayO ₴1DEV (docker)7 09:44:56 on ttys006H82APP (-zsh)-zsh• 84screenpipe*•$5-zsh₴6Poetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentsPoetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentslukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-20773-fix-automated-reports-user-pilot-tracking) $ devroot@docker_lamp_1:/home/jiminny# php artisan crm:sync-opportunity --teamId=2 --from='2026-05-01 00:00:00*Syncing opportunity for HubspotYour HubSpotaccount has become disconnected. Please login to Jiminny to reconnect. skipping...root@docker_lamp_1:/home/jiminny# php artisan jiminny:token-info -A 1499 -RDEVaccess_tokenCFPAsBNZxoDp5kAcRyeB1QoE5SM7DSgNuYTFSAFoAYABo3tj9DHAAeAACNeR-JHgMxIZQLNQMl8kQEwrAgwACAkUAhIJBB4BAQEDBxiCiYwCIN7Y_Qwo0qwCMhTnG549n-YtNuc1jgj-2AsLPSmw3DoyQLNQM18kQEwrAiUACBkGawEFThwBARIBAQEEATEEAQEBAQEBAQEBAQUBEggBAQEBAY1access_token_expires_at= 2026-05-07 11:41:20refresh_token => d5ab04e2-2109-4c0b-b513-8cba1dd54371refresh_token_expires_at =root@docker_lamp_1:/home/jiminny#...
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/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778153013980_m2.jpg...
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PostmancaltVIewWindowmelpHubSpot rate limit implem PostmancaltVIewWindowmelpHubSpot rate limit implementation strategyX-HubSpot-RateLimit-Max: 190X-HubSpot-RateLimit-Remaining: 189X-Hubspot-kateL1m1t-Incerval-M1111seconds: 10000That tells you the portal is on 190 calls per 10 seconds. The Remaining value is live — fire thesame call again immediately and it drops to 188. etc. There is no endpoint that returns this inJSON; the headers are the only channel.Search: not visible. At all.• No endpoint exposes the 5/sec search limit• Search responses ( POST /crm/v3/objects/{type}/search ) deliberately strip all x-HubSpot-RateLimit-* headers, so you can't read it from a search call either.• The 5/sec figure is a documented constant — you assume it, you don't query it.• The only way to "observe" the search limit empirically is to deliberately exceed it andinspect the 429 response, where the body contains "policyName": "SECONDLY" and"message": "You have reached your secondly limit." — which is what you alreadysaw in your original error.So your rull per-portal picture from the APl 1S:uimitlow to see it via PostmanDailyGET /account-info/v3/api-usage/daily/private-apps (body)Any non-search call (response Headers t. VKeep going in Claude CodeSwitch to Claude Code and let Claude work directly in your repo,running and testing as it goes."results": ["name": "private-apps-api-calls-daily","currentusage": 0."2026-05-07T11:23:01. 362Z","fetchStatus":ncueeecen"resetsAt": "2026-05-08T04:00: 00Z"v COLLECtIONs› CRM ObjectsCRM Owners> CRM Pipelines> DealsEngagements• D OLD ENGAGEMENTSGET list meetingsPost search tasksGET read call> Post search callsGET list callsPoST meetings scheduledGET get meetingPost get link to task> POST Create Contact with Association› HubspotJournal & webhoooks v4POST Get tokenGET get subscriptionsPOST create subscriptionGET Journal earliestGET Journal latestGET https:/ubspot-webhooks-journal-nal.s.GET neyt offsetPOST aet Token prodDEL DELETE SUBSCRIPTION PER PORTALGET DEAL WITH HISTORY PROPERTIES V3› OAuth> Properties> PESCAPCHV SEARCHIPoST search contact by phonePost search contact ov emailliPOST search meetingsPOST search notes> post Soarch calle v2POST Search related meetinas v3PoSt search deals>ENVIRONMENTS> SPFCS> FLOWS@ Connect Git = Concole 5.) TerminCNeR-JHaMxlZoiNd.DescriotionBodyCookiesHeadss 20JSONvPreview? Visualize"results":"name": "private-apps-api-calls-daily*."2026-05-07T11:23:01.3622"resetsAt": "2026-05-08T04:00:002'200 OK • 190 ms • 1.2 KB •C| .•=a1D0Opus 4.7 AdaptiveXx Hubspot vQ SearchYour 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 collaborationGET next off. • POST search •HTTP https:pi.hubapi.com/account-info/v3/api-usaqe/daily/private-appshttps://api.hubapi.com/account-info/v3/api-usage/daily/private-apps= DocsParams Authorization • Headers 9 Body Scripts Settinas© 9 hiddenSO WOSupport Daily • in 37 mGET httos://:No environng SaveCookiesBulk Edit Presets100% C4)* AIVariables in requestG tokenAll variablesThu 7 May 14:23:33Globals Vault Toos S 0 00...
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PostmancaltVIewWindowmelpHubSpot rate limit implem PostmancaltVIewWindowmelpHubSpot rate limit implementation strategyX-HubSpot-RateLimit-Max: 190X-HubSpot-RateLimit-Remaining: 189X-Hubspot-kateL1m1t-Incerval-M1111seconds: 10000That tells you the portal is on 190 calls per 10 seconds. The Remaining value is live — fire thesame call again immediately and it drops to 188. etc. There is no endpoint that returns this inJSON; the headers are the only channel.Search: not visible. At all.• No endpoint exposes the 5/sec search limit• Search responses ( POST /crm/v3/objects/{type}/search ) deliberately strip all x-HubSpot-RateLimit-* headers, so you can't read it from a search call either.• The 5/sec figure is a documented constant — you assume it, you don't query it.• The only way to "observe" the search limit empirically is to deliberately exceed it andinspect the 429 response, where the body contains "policyName": "SECONDLY" and"message": "You have reached your secondly limit." — which is what you alreadysaw in your original error.So your rull per-portal picture from the APl 1S:uimitlow to see it via PostmanDailyGET /account-info/v3/api-usage/daily/private-apps (body)Any non-search call (response Headers t. VKeep going in Claude CodeSwitch to Claude Code and let Claude work directly in your repo,running and testing as it goes."results": ["name": "private-apps-api-calls-daily","currentusage": 0."2026-05-07T11:23:01. 362Z","fetchStatus":ncueeecen"resetsAt": "2026-05-08T04:00: 00Z"v COLLECtIONs› CRM ObjectsCRM Owners> CRM Pipelines> DealsEngagements• D OLD ENGAGEMENTSGET list meetingsPost search tasksGET read call> Post search callsGET list callsPoST meetings scheduledGET get meetingPost get link to task> POST Create Contact with Association› HubspotJournal & webhoooks v4POST Get tokenGET get subscriptionsPOST create subscriptionGET Journal earliestGET Journal latestGET https:/ubspot-webhooks-journal-nal.s.GET neyt offsetPOST aet Token prodDEL DELETE SUBSCRIPTION PER PORTALGET DEAL WITH HISTORY PROPERTIES V3› OAuth> Properties> PESCAPCHV SEARCHIPoST search contact by phonePost search contact ov emailliPOST search meetingsPOST search notes> post Soarch calle v2POST Search related meetinas v3PoSt search deals>ENVIRONMENTS> SPFCS> FLOWS@ Connect Git = Concole 5.) TerminCNeR-JHaMxlZoiNd.DescriotionBodyCookiesHeadss 20JSONvPreview? Visualize"results":"name": "private-apps-api-calls-daily*."2026-05-07T11:23:01.3622"resetsAt": "2026-05-08T04:00:002'200 OK • 190 ms • 1.2 KB •C| .•=a1D0Opus 4.7 AdaptiveXx Hubspot vQ SearchYour 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 collaborationGET next off. • POST search •HTTP https:pi.hubapi.com/account-info/v3/api-usaqe/daily/private-appshttps://api.hubapi.com/account-info/v3/api-usage/daily/private-apps= DocsParams Authorization • Headers 9 Body Scripts Settinas© 9 hiddenSO WOSupport Daily • in 37 mGET httos://:No environng SaveCookiesBulk Edit Presets100% C4)* AIVariables in requestG tokenAll variablesThu 7 May 14:23:33Globals Vault Toos S 0 00...
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iTerm2ShellEditViewSessionScriptsProfilesWindowHel iTerm2ShellEditViewSessionScriptsProfilesWindowHelp(ahol§ Support Daily - in 37 m100% [Thu 7 May 14:23:35DEV (docker)83DOCKERLast login: Thu MayO ₴1DEV (docker)7 09:44:56 on ttys006H82APP (-zsh)-zsh• 84screenpipe*•$5-zsh₴6Poetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentsPoetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentslukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-20773-fix-automated-reports-user-pilot-tracking) $ devroot@docker_lamp_1:/home/jiminny# php artisan crm:sync-opportunity --teamId=2 --from='2026-05-01 00:00:00*Syncing opportunity for HubspotYour HubSpotaccount has become disconnected. Please login to Jiminny to reconnect. skipping...root@docker_lamp_1:/home/jiminny# php artisan jiminny:token-info -A 1499 -RDEVaccess_tokenCFPAsBNZxoDp5kAcRyeB1QoE5SM7DSgNuYTFSAFoAYABo3tj9DHAAeAACNeR-JHgMxIZQLNQMl8kQEwrAgwACAkUAhIJBB4BAQEDBxiCiYwCIN7Y_Qwo0qwCMhTnG549n-YtNuc1jgj-2AsLPSmw3DoyQLNQM18kQEwrAiUACBkGawEFThwBARIBAQEEATEEAQEBAQEBAQEBAQUBEggBAQEBAY1access_token_expires_at= 2026-05-07 11:41:20refresh_token => d5ab04e2-2109-4c0b-b513-8cba1dd54371refresh_token_expires_at =root@docker_lamp_1:/home/jiminny#...
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iTerm2ShellEditViewSessionScriptsProfilesWindowHel iTerm2ShellEditViewSessionScriptsProfilesWindowHelp(ahol§ Support Daily - in 37 m100% [Thu 7 May 14:23:35DEV (docker)83DOCKERLast login: Thu MayO ₴1DEV (docker)7 09:44:56 on ttys006H82APP (-zsh)-zsh• 84screenpipe*•$5-zsh₴6Poetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentsPoetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentslukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-20773-fix-automated-reports-user-pilot-tracking) $ devroot@docker_lamp_1:/home/jiminny# php artisan crm:sync-opportunity --teamId=2 --from='2026-05-01 00:00:00*Syncing opportunity for HubspotYour HubSpotaccount has become disconnected. Please login to Jiminny to reconnect. skipping...root@docker_lamp_1:/home/jiminny# php artisan jiminny:token-info -A 1499 -RDEVaccess_tokenCFPAsBNZxoDp5kAcRyeB1QoE5SM7DSgNuYTFSAFoAYABo3tj9DHAAeAACNeR-JHgMxIZQLNQMl8kQEwrAgwACAkUAhIJBB4BAQEDBxiCiYwCIN7Y_Qwo0qwCMhTnG549n-YtNuc1jgj-2AsLPSmw3DoyQLNQM18kQEwrAiUACBkGawEFThwBARIBAQEEATEEAQEBAQEBAQEBAQUBEggBAQEBAY1access_token_expires_at= 2026-05-07 11:41:20refresh_token => d5ab04e2-2109-4c0b-b513-8cba1dd54371refresh_token_expires_at =root@docker_lamp_1:/home/jiminny#...
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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:
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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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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
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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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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....
|
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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). 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"}]...
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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
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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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....
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/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778153016706_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
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...
|
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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). 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"},{"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"}]...
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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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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
[
2
]
)
local
burst_used
=
redis
.
call
(
'ZCARD'
,
KEYS
[
1
]
)
local
daily_used
=
tonumber
(
redis
.
call
(
'GET'
,
KEYS
[
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Claude
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Claude
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True
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monitor_2
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HubSpot rate limit handling with executeRequest
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HubSpot rate limit implementation strategy, rename chat
HubSpot rate limit implementation strategy
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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?
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
[
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...
|
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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. 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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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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"},{"role":"AXStaticText","text":"Redis ops/sec:","depth":27,"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":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Per-request overhead:","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"~0.3–1.0 ms for an","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"EVALSHA","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.
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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
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
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:
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...
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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
=
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...
|
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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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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
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 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...
|
2526
|
NULL
|
NULL
|
NULL
|
|
2530
|
NULL
|
0
|
2026-05-07T11:24:07.898992+00:00
|
/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778153047898_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.
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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
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:
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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...
|
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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. 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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. 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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. 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 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"}]...
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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
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...
|
2528
|
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/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778153056757_m2.jpg...
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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
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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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. 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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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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
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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0
|
2026-05-07T11:24:37.463802+00:00
|
/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778153077463_m1.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
]
)
-
ARGV
[
1
]
}
end
if
daily_used
>=
tonumber
(
ARGV
[
4
]
)
then...
|
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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. 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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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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 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...
|
NULL
|
NULL
|
NULL
|
NULL
|
|
2533
|
108
|
1
|
2026-05-07T11:24:47.236384+00:00
|
/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778153087236_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
'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'...
|
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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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Tell caller how long to sleep until oldest entry 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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.
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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
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 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'...
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iTerm2ShellEditViewSessionScriptsProfilesWindowHel iTerm2ShellEditViewSessionScriptsProfilesWindowHelp(ah]• Support Daily • in 36 m100% [Thu 7 May 14:24:54DEV (docker)83DOCKERLast login: Thu MayO ₴1DEV (docker)7 09:44:56 on ttys006H82APP (-zsh)-zsh• 84screenpipe*•$5-zsh₴6Poetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentsPoetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentslukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-20773-fix-automated-reports-user-pilot-tracking) $ devroot@docker_lamp_1:/home/jiminny# php artisan crm:sync-opportunity --teamId=2 --from='2026-05-01 00:00:00*Syncing opportunity for HubspotYour HubSpotaccount has become disconnected. Please login to Jiminny to reconnect. skipping...root@docker_lamp_1:/home/jiminny# php artisan jiminny:token-info -A 1499 -RDEVaccess_tokenCFPAsBNZxoDp5kAcRyeB1QoE5SM7DSgNuYTFSAFoAYABo3tj9DHAAeAACNeR-JHgMxIZQLNQMl8kQEwrAgwACAkUAhIJBB4BAQEDBxiCiYwCIN7Y_Qwo0qwCMhTnG549n-YtNuc1jgj-2AsLPSmw3DoyQLNQM18kQEwrAiUACBkGawEFThwBARIBAQEEATEEAQEBAQEBAQEBAQUBEggBAQEBAY1access_token_expires_at= 2026-05-07 11:41:20refresh_token => d5ab04e2-2109-4c0b-b513-8cba1dd54371refresh_token_expires_at =root@docker_lamp_1:/home/jiminny#...
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iTerm2ShellEditViewSessionScriptsProfilesWindowHel iTerm2ShellEditViewSessionScriptsProfilesWindowHelp(ah]• Support Daily • in 36 m100% [Thu 7 May 14:24:54DEV (docker)83DOCKERLast login: Thu MayO ₴1DEV (docker)7 09:44:56 on ttys006H82APP (-zsh)-zsh• 84screenpipe*•$5-zsh₴6Poetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentsPoetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentslukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-20773-fix-automated-reports-user-pilot-tracking) $ devroot@docker_lamp_1:/home/jiminny# php artisan crm:sync-opportunity --teamId=2 --from='2026-05-01 00:00:00*Syncing opportunity for HubspotYour HubSpotaccount has become disconnected. Please login to Jiminny to reconnect. skipping...root@docker_lamp_1:/home/jiminny# php artisan jiminny:token-info -A 1499 -RDEVaccess_tokenCFPAsBNZxoDp5kAcRyeB1QoE5SM7DSgNuYTFSAFoAYABo3tj9DHAAeAACNeR-JHgMxIZQLNQMl8kQEwrAgwACAkUAhIJBB4BAQEDBxiCiYwCIN7Y_Qwo0qwCMhTnG549n-YtNuc1jgj-2AsLPSmw3DoyQLNQM18kQEwrAiUACBkGawEFThwBARIBAQEEATEEAQEBAQEBAQEBAQUBEggBAQEBAY1access_token_expires_at= 2026-05-07 11:41:20refresh_token => d5ab04e2-2109-4c0b-b513-8cba1dd54371refresh_token_expires_at =root@docker_lamp_1:/home/jiminny#...
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Claude FileEditVIewWindowHelp•• еHubSpot rate limi Claude FileEditVIewWindowHelp•• еHubSpot rate limit implementation strategy"rosuite": 1"name": "private-apps-api-calls-daily","usageLimit": 1000000,"currentUsage": 0"collectedAt": "2026-05-07T11:23:01.3627".*** Clarifving OAuth dailv limits versus private app quotasKeep going in Claude CodeSwitch to Claude Code and let Claude work directlv in vour reno.running and testing as it goes.Write a message…Opus 4.7 Adaptive vClaude ic Aand can make mistakas Plesce double-chock racnoncac)Thu 7 May 14:24:54UparadeQ Searchh. rou fetain eoltinig decessand other members are read-only. View team permissions to see who can edit, or upgrade to restore collaborationGET read ciHTTP https:pi.hubapi.com/account-info/v3/api-usage/daily/private-appshttps://api.hubapi.com/account-info/v3/api-usage/daily/private-apps= DocsParams Authorization • Headers 9 Body Scripts Settings© 9 hiddenValueValueGET httos://:Descriotionsuppont Dally • In 3omNo environmentv~ SaveCookiesBulk Edit Presets v100% L24* AIVariables in requestG token› All VarlablesCNeR-JHaMxlZoiNd.Bodyheaders 20 lest Resultsistatusdatecontent-tvoecf-raycf-cache-statusstrict-transport-securityaccess-control-allow-credentialsserver-timingyacontent-tune.ontionsx-hubspot-correlation-idx-hubspot-ratelimit-interval-millisecondswealnenrwoltmeohnhy-hubsnot-ratelimit-secondlv-remainingservecontent-encodind200 OK • 190 ms • 1.2 KB •C| .•Thu. 07 May 2026 11:23:01 GMTIapplication/ison:charset=utf-89f7fd9a4cfcA2d79-SoFDYNAMICmax-age=31536000; includeSubDomains: preloadorigin, Accept-Encodingralsehaid.daea-"0100022d.424h.2122.0222.179Anfdd9790" Afridoen-"06744004d2602402-100"nosniff019e022d-434b-71c3-922a-178cafdd878ef"endpoints":[("url".*httos:WWa.nel.cloudflare.comVreport\v4?s=ghtpusin4YfDIMG27vDtmAf.f"cuecoss fraction".0.01 "ronort to"."of_nolkimay aag".604900}cloudflareGlobals Vault Tools?000...
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Claude FileEditVIewWindowHelp•• еHubSpot rate limi Claude FileEditVIewWindowHelp•• еHubSpot rate limit implementation strategy"rosuite": 1"name": "private-apps-api-calls-daily","usageLimit": 1000000,"currentUsage": 0"collectedAt": "2026-05-07T11:23:01.3627".*** Clarifving OAuth dailv limits versus private app quotasKeep going in Claude CodeSwitch to Claude Code and let Claude work directlv in vour reno.running and testing as it goes.Write a message…Opus 4.7 Adaptive vClaude ic Aand can make mistakas Plesce double-chock racnoncac)Thu 7 May 14:24:54UparadeQ Searchh. rou fetain eoltinig decessand other members are read-only. View team permissions to see who can edit, or upgrade to restore collaborationGET read ciHTTP https:pi.hubapi.com/account-info/v3/api-usage/daily/private-appshttps://api.hubapi.com/account-info/v3/api-usage/daily/private-apps= DocsParams Authorization • Headers 9 Body Scripts Settings© 9 hiddenValueValueGET httos://:Descriotionsuppont Dally • In 3omNo environmentv~ SaveCookiesBulk Edit Presets v100% L24* AIVariables in requestG token› All VarlablesCNeR-JHaMxlZoiNd.Bodyheaders 20 lest Resultsistatusdatecontent-tvoecf-raycf-cache-statusstrict-transport-securityaccess-control-allow-credentialsserver-timingyacontent-tune.ontionsx-hubspot-correlation-idx-hubspot-ratelimit-interval-millisecondswealnenrwoltmeohnhy-hubsnot-ratelimit-secondlv-remainingservecontent-encodind200 OK • 190 ms • 1.2 KB •C| .•Thu. 07 May 2026 11:23:01 GMTIapplication/ison:charset=utf-89f7fd9a4cfcA2d79-SoFDYNAMICmax-age=31536000; includeSubDomains: preloadorigin, Accept-Encodingralsehaid.daea-"0100022d.424h.2122.0222.179Anfdd9790" Afridoen-"06744004d2602402-100"nosniff019e022d-434b-71c3-922a-178cafdd878ef"endpoints":[("url".*httos:WWa.nel.cloudflare.comVreport\v4?s=ghtpusin4YfDIMG27vDtmAf.f"cuecoss fraction".0.01 "ronort to"."of_nolkimay aag".604900}cloudflareGlobals Vault Tools?000...
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iTerm2ShellEditViewSessionScriptsProfilesWindowHel iTerm2ShellEditViewSessionScriptsProfilesWindowHelp(ah]• Support Daily • in 36 m100% [Thu 7 May 14:24:55DEV (docker)83DOCKERLast login: Thu MayO ₴1DEV (docker)7 09:44:56 on ttys006H82APP (-zsh)-zsh• 84screenpipe*•$5-zsh₴6Poetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentsPoetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentslukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-20773-fix-automated-reports-user-pilot-tracking) $ devroot@docker_lamp_1:/home/jiminny# php artisan crm:sync-opportunity --teamId=2 --from='2026-05-01 00:00:00*Syncing opportunity for HubspotYour HubSpotaccount has become disconnected. Please login to Jiminny to reconnect. skipping...root@docker_lamp_1:/home/jiminny# php artisan jiminny:token-info -A 1499 -RDEVaccess_tokenCFPAsBNZxoDp5kAcRyeB1QoE5SM7DSgNuYTFSAFoAYABo3tj9DHAAeAACNeR-JHgMxIZQLNQMl8kQEwrAgwACAkUAhIJBB4BAQEDBxiCiYwCIN7Y_Qwo0qwCMhTnG549n-YtNuc1jgj-2AsLPSmw3DoyQLNQM18kQEwrAiUACBkGawEFThwBARIBAQEEATEEAQEBAQEBAQEBAQUBEggBAQEBAY1access_token_expires_at= 2026-05-07 11:41:20refresh_token => d5ab04e2-2109-4c0b-b513-8cba1dd54371refresh_token_expires_at =root@docker_lamp_1:/home/jiminny#...
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iTerm2ShellEditViewSessionScriptsProfilesWindowHel iTerm2ShellEditViewSessionScriptsProfilesWindowHelp(ah]• Support Daily • in 36 m100% [Thu 7 May 14:24:55DEV (docker)83DOCKERLast login: Thu MayO ₴1DEV (docker)7 09:44:56 on ttys006H82APP (-zsh)-zsh• 84screenpipe*•$5-zsh₴6Poetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentsPoetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentslukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-20773-fix-automated-reports-user-pilot-tracking) $ devroot@docker_lamp_1:/home/jiminny# php artisan crm:sync-opportunity --teamId=2 --from='2026-05-01 00:00:00*Syncing opportunity for HubspotYour HubSpotaccount has become disconnected. Please login to Jiminny to reconnect. skipping...root@docker_lamp_1:/home/jiminny# php artisan jiminny:token-info -A 1499 -RDEVaccess_tokenCFPAsBNZxoDp5kAcRyeB1QoE5SM7DSgNuYTFSAFoAYABo3tj9DHAAeAACNeR-JHgMxIZQLNQMl8kQEwrAgwACAkUAhIJBB4BAQEDBxiCiYwCIN7Y_Qwo0qwCMhTnG549n-YtNuc1jgj-2AsLPSmw3DoyQLNQM18kQEwrAiUACBkGawEFThwBARIBAQEEATEEAQEBAQEBAQEBAQUBEggBAQEBAY1access_token_expires_at= 2026-05-07 11:41:20refresh_token => d5ab04e2-2109-4c0b-b513-8cba1dd54371refresh_token_expires_at =root@docker_lamp_1:/home/jiminny#...
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PostmanFileEditViewWindowHelpHubSpot rate limit im PostmanFileEditViewWindowHelpHubSpot rate limit implementation strategy v"rosuite": 1"name": "private-apps-api-calls-daily","usageLimit": 1000000,"currentUsage": 0"collectedAt": "2026-05-07T11:23:01.3627".Xx Hubspot vQ SearchYour 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. • POSTX POST Reada•HTTP https:pi.hubapi.com/account-info/v3/api-usaqe/daily/private-appshttps://api.hubapi.com/account-info/v3/api-usage/daily/private-apps= DocsParams Authorization • Headers 9 Body Scripts Settinas© 9 hiddenValueValueGET httos://:suppont Dally • In 3omNo environmentv~ SaveCookiesBulk Edit Presets v100% L24* AIVariables in requestG token› All VarlablesThu 7 May 14:24:56UparadeCNeR-JHaMxlZoiNd.** Clarifving OAuth dailv limits versus private app quotasKeep going in Claude CodeSwitch to Claude Code and let Claude work directlv in vour reno.running and testing as it goes.Write a message…Opus 4.7 Adaptive vClaudo ic Aland can mako mictakas Plesce double-chock racnoncac)v COLLECtIONs› CRM ObjectsCRM Owners> CRM Pipelines> DealsEngagements• D OLD ENGAGEMENTSGET list meetingspost coarch tackeGET read call> PosT search callsGET list callsPOST meetings scheduledGET get meetingPOST get link to task> POST Create Contact with Association> HubsnotJournal & webhoooks v4POSt Get tokenPOST create subscrintionGET Journal earliestGET Journal latestGeT https:/ubspot-webhooks-journal-nal.s.GET neyt offsetPOST aet Token prodDEL DELETE SURSCRIPTION DER PORTAL.GET DEAL WITH HISTORY PROPERTIES V3› OAuth> Properties> PESCAPCHV SEARCHIPOST search contact by phonePost search contact ov emailliPOST search meetingsPOST search notespost Soarch calle v2POST Search related meetings v3post coarch doalsENMIDANMENTS> SPFCSELOWS@ Connect Git = Concole 5.) TerminaDescriotionBody Cookies 1Headers 20 lest Resultsistatusdatecontent-tvoecf-rayef-cache-statusstrict-transport-securityaccess-control-allow-credentialsserver-timingyacontent-tune.ontionsx-hubspot-correlation-idx-hubspot-ratelimit-interval-millisecondswwlnerrwoltmeohanhy-hubsnot-ratelimit-secondlv-remainingservecontent-encodind200 OK • 190 ms • 1.2 KB •C| .•Thu. 07 May 2026 11:23:01 GMTIapplication/ison:charset=utf-89f7fd9a4cfcA2d79-SoFDYNAMICmax-age=31536000: includesubDomains: preloadorigin, Accept-Encodingtalsehaid.daea-"0100022d.424h.2122.0222.179Anfdd9790" Afridocn-"06744004d2602402-100"nosniff019e022d-434b-71c3-922a-178cafdd878e108f"endpoints":[("url":*httos:W/a.nel.cloudflare.comVreportVv4?s=ahtpusin4YfDIMG27vDtmAf.f"cuecoss fraction".0.01 "ronort to"."of_nolkimay aac".604900}cloudflareGlobals Vault Tools?000...
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PostmanFileEditViewWindowHelpHubSpot rate limit im PostmanFileEditViewWindowHelpHubSpot rate limit implementation strategy v"rosuite": 1"name": "private-apps-api-calls-daily","usageLimit": 1000000,"currentUsage": 0"collectedAt": "2026-05-07T11:23:01.3627".Xx Hubspot vQ SearchYour 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. • POSTX POST Reada•HTTP https:pi.hubapi.com/account-info/v3/api-usaqe/daily/private-appshttps://api.hubapi.com/account-info/v3/api-usage/daily/private-apps= DocsParams Authorization • Headers 9 Body Scripts Settinas© 9 hiddenValueValueGET httos://:suppont Dally • In 3omNo environmentv~ SaveCookiesBulk Edit Presets v100% L24* AIVariables in requestG token› All VarlablesThu 7 May 14:24:56UparadeCNeR-JHaMxlZoiNd.** Clarifving OAuth dailv limits versus private app quotasKeep going in Claude CodeSwitch to Claude Code and let Claude work directlv in vour reno.running and testing as it goes.Write a message…Opus 4.7 Adaptive vClaudo ic Aland can mako mictakas Plesce double-chock racnoncac)v COLLECtIONs› CRM ObjectsCRM Owners> CRM Pipelines> DealsEngagements• D OLD ENGAGEMENTSGET list meetingspost coarch tackeGET read call> PosT search callsGET list callsPOST meetings scheduledGET get meetingPOST get link to task> POST Create Contact with Association> HubsnotJournal & webhoooks v4POSt Get tokenPOST create subscrintionGET Journal earliestGET Journal latestGeT https:/ubspot-webhooks-journal-nal.s.GET neyt offsetPOST aet Token prodDEL DELETE SURSCRIPTION DER PORTAL.GET DEAL WITH HISTORY PROPERTIES V3› OAuth> Properties> PESCAPCHV SEARCHIPOST search contact by phonePost search contact ov emailliPOST search meetingsPOST search notespost Soarch calle v2POST Search related meetings v3post coarch doalsENMIDANMENTS> SPFCSELOWS@ Connect Git = Concole 5.) TerminaDescriotionBody Cookies 1Headers 20 lest Resultsistatusdatecontent-tvoecf-rayef-cache-statusstrict-transport-securityaccess-control-allow-credentialsserver-timingyacontent-tune.ontionsx-hubspot-correlation-idx-hubspot-ratelimit-interval-millisecondswwlnerrwoltmeohanhy-hubsnot-ratelimit-secondlv-remainingservecontent-encodind200 OK • 190 ms • 1.2 KB •C| .•Thu. 07 May 2026 11:23:01 GMTIapplication/ison:charset=utf-89f7fd9a4cfcA2d79-SoFDYNAMICmax-age=31536000: includesubDomains: preloadorigin, Accept-Encodingtalsehaid.daea-"0100022d.424h.2122.0222.179Anfdd9790" Afridocn-"06744004d2602402-100"nosniff019e022d-434b-71c3-922a-178cafdd878e108f"endpoints":[("url":*httos:W/a.nel.cloudflare.comVreportVv4?s=ahtpusin4YfDIMG27vDtmAf.f"cuecoss fraction".0.01 "ronort to"."of_nolkimay aac".604900}cloudflareGlobals Vault Tools?000...
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2026-05-07T11:24:59.131278+00:00
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/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778153099131_m2.jpg...
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iTerm2
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True
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PostmanFileEditViewWindowHelpHubSpot rate limit im PostmanFileEditViewWindowHelpHubSpot rate limit implementation strategy"results": ["name": "private-apps-api-calls-daily","usageLimit": 1000000,"currentUsage": 0,"collectedAt": "2026-05-07T11:23:01.362Z",* Clarifying that 1M daily limit applies only to private apps, not OAuth40hhlsuppont Dally • In 3omQ SearchYour 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.SEARCH › search deals= DocsAuthorization • Headers 11 Body • ScriptsSettingserawO binary GraphQL JSON~rilters":"value": 17730411243623002055809"hs_call _direction",No environmentvSaveShare** Dookos° Schema BeautifyBOdV200 OK • 244 ms • 1.71 KB - G| eg. Save Response ..JSONvPreviewe. Visualize==aID8100% L2VAIlVariables in requestG tokenInu / May 14-24:09UparadeCNeR-JHgMxIZQINQ...Keep going in Claude CodeSwitch to Claude Code and let Claude work directlv in vour reno.running and testing as it goes.Write a message…Opus 4.7 AdaptiveClaudo ic Aand can mako mictakas Plesce double-chock racnoncocv COLLECtIONs› CRM ObjectsCRM owners> CRM Pipelines> DealsEngagements• D OLD ENGAGEMENTSGET list meetingsPost search tasksGET read call> Post search callsGET list callsPoST meetings scheduledGET aet meetinaPost get link to task> POST Create Contact with Association› HubspotJournal & webhoooks v4POST Get tokenPOST create subscriptionGET Journal earliestGET Journal latestGET https:/ubspot-webhooks-journal-nal.s.GET next offsetPOST aet Token prodDEL DELETE SUBSCRIPTION PER PORTALGET DEAL WITH HISTORY PROPERTIES V3› OAuth› Properties> PESCAPCHV SEARCHIPoST search contact by phonePost search contact ov emailliPOST search meetingsPOST search notes> PosT Search calls v3POST Search related meetings v3>ENVIRONMENTS> SPFCS>FLOWS@ Connect Git = ConcoldE Term"arontodoto", 12019-06.16707.22-66 2027"waA1O-AG.44TAR.20.40 0227""2026-05-04705:33:47.3402","hubsoot owner id": "579583316""updatedAt: 2026-05-04105:33:47.3402"archived": talse."ux": "httos:aop.hubspot.com/contacts/4392066/xecoxd/0-3/297846423'"id". "181281563".#nronortjoc".S#crontodato". #2010-01.22716-21-10 2607""hs_lastmodifieddate": "2026-04-19T16:14:05.694Z","updatedAt": "2026-04- 19T16:14:05.6942"....
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-4034375739420454276
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PostmanFileEditViewWindowHelpHubSpot rate limit im PostmanFileEditViewWindowHelpHubSpot rate limit implementation strategy"results": ["name": "private-apps-api-calls-daily","usageLimit": 1000000,"currentUsage": 0,"collectedAt": "2026-05-07T11:23:01.362Z",* Clarifying that 1M daily limit applies only to private apps, not OAuth40hhlsuppont Dally • In 3omQ SearchYour 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.SEARCH › search deals= DocsAuthorization • Headers 11 Body • ScriptsSettingserawO binary GraphQL JSON~rilters":"value": 17730411243623002055809"hs_call _direction",No environmentvSaveShare** Dookos° Schema BeautifyBOdV200 OK • 244 ms • 1.71 KB - G| eg. Save Response ..JSONvPreviewe. Visualize==aID8100% L2VAIlVariables in requestG tokenInu / May 14-24:09UparadeCNeR-JHgMxIZQINQ...Keep going in Claude CodeSwitch to Claude Code and let Claude work directlv in vour reno.running and testing as it goes.Write a message…Opus 4.7 AdaptiveClaudo ic Aand can mako mictakas Plesce double-chock racnoncocv COLLECtIONs› CRM ObjectsCRM owners> CRM Pipelines> DealsEngagements• D OLD ENGAGEMENTSGET list meetingsPost search tasksGET read call> Post search callsGET list callsPoST meetings scheduledGET aet meetinaPost get link to task> POST Create Contact with Association› HubspotJournal & webhoooks v4POST Get tokenPOST create subscriptionGET Journal earliestGET Journal latestGET https:/ubspot-webhooks-journal-nal.s.GET next offsetPOST aet Token prodDEL DELETE SUBSCRIPTION PER PORTALGET DEAL WITH HISTORY PROPERTIES V3› OAuth› Properties> PESCAPCHV SEARCHIPoST search contact by phonePost search contact ov emailliPOST search meetingsPOST search notes> PosT Search calls v3POST Search related meetings v3>ENVIRONMENTS> SPFCS>FLOWS@ Connect Git = ConcoldE Term"arontodoto", 12019-06.16707.22-66 2027"waA1O-AG.44TAR.20.40 0227""2026-05-04705:33:47.3402","hubsoot owner id": "579583316""updatedAt: 2026-05-04105:33:47.3402"archived": talse."ux": "httos:aop.hubspot.com/contacts/4392066/xecoxd/0-3/297846423'"id". "181281563".#nronortjoc".S#crontodato". #2010-01.22716-21-10 2607""hs_lastmodifieddate": "2026-04-19T16:14:05.694Z","updatedAt": "2026-04- 19T16:14:05.6942"....
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3
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2026-05-07T11:24:59.329090+00:00
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/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778153099329_m1.jpg...
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iTerm2
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True
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monitor_1
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iTerm2ShellEditViewSessionScriptsProfilesWindowHel iTerm2ShellEditViewSessionScriptsProfilesWindowHelp(ah]• Support Daily • in 36 m100% [Thu 7 May 14:24:59DEV (docker)T81DOCKERLast login: Thu MayO ₴1DEV (docker)7 09:44:56 on ttys006H82APP (-zsh)83-zsh• 84screenpipe*•$5-zsh₴6Poetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentsPoetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentslukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-20773-fix-automated-reports-user-pilot-tracking) $ devroot@docker_lamp_1:/home/jiminny# php artisan crm:sync-opportunity --teamId=2 --from='2026-05-01 00:00:00*Syncing opportunity for HubspotYour HubSpotaccount has become disconnected. Please login to Jiminny to reconnect. skipping...root@docker_lamp_1:/home/jiminny# php artisan jiminny:token-info -A 1499 -RDEVaccess_tokenCFPAsBNZxoDp5kAcRyeB1QoE5SM7DSgNuYTFSAFoAYABo3tj9DHAAeAACNeR-JHgMxIZQLNQMl8kQEwrAgwACAkUAhIJBB4BAQEDBxiCiYwCIN7Y_Qwo0qwCMhTnG549n-YtNuc1jgj-2AsLPSmw3DoyQLNQM18kQEwrAiUACBkGawEFThwBARIBAQEEATEEAQEBAQEBAQEBAQUBEggBAQEBAY1access_token_expires_at= 2026-05-07 11:41:20refresh_token => d5ab04e2-2109-4c0b-b513-8cba1dd54371refresh_token_expires_at =root@docker_lamp_1:/home/jiminny#...
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-8148214480034990038
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iTerm2ShellEditViewSessionScriptsProfilesWindowHel iTerm2ShellEditViewSessionScriptsProfilesWindowHelp(ah]• Support Daily • in 36 m100% [Thu 7 May 14:24:59DEV (docker)T81DOCKERLast login: Thu MayO ₴1DEV (docker)7 09:44:56 on ttys006H82APP (-zsh)83-zsh• 84screenpipe*•$5-zsh₴6Poetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentsPoetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentslukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-20773-fix-automated-reports-user-pilot-tracking) $ devroot@docker_lamp_1:/home/jiminny# php artisan crm:sync-opportunity --teamId=2 --from='2026-05-01 00:00:00*Syncing opportunity for HubspotYour HubSpotaccount has become disconnected. Please login to Jiminny to reconnect. skipping...root@docker_lamp_1:/home/jiminny# php artisan jiminny:token-info -A 1499 -RDEVaccess_tokenCFPAsBNZxoDp5kAcRyeB1QoE5SM7DSgNuYTFSAFoAYABo3tj9DHAAeAACNeR-JHgMxIZQLNQMl8kQEwrAgwACAkUAhIJBB4BAQEDBxiCiYwCIN7Y_Qwo0qwCMhTnG549n-YtNuc1jgj-2AsLPSmw3DoyQLNQM18kQEwrAiUACBkGawEFThwBARIBAQEEATEEAQEBAQEBAQEBAQUBEggBAQEBAY1access_token_expires_at= 2026-05-07 11:41:20refresh_token => d5ab04e2-2109-4c0b-b513-8cba1dd54371refresh_token_expires_at =root@docker_lamp_1:/home/jiminny#...
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2026-05-07T11:25:00.660548+00:00
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/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778153100660_m1.jpg...
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iTerm2ShellEditViewSessionScriptsProfilesWindowHel iTerm2ShellEditViewSessionScriptsProfilesWindowHelp(ah]• Support Daily • in 36 m100% [Thu 7 May 14:25:00DEV (docker)T81DOCKERLast login: Thu MayO ₴1DEV (docker)7 09:44:56 on ttys006H82APP (-zsh)83-zsh• 84screenpipe*•$5-zsh₴6Poetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentsPoetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentslukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-20773-fix-automated-reports-user-pilot-tracking) $ devroot@docker_lamp_1:/home/jiminny# php artisan crm:sync-opportunity --teamId=2 --from='2026-05-01 00:00:00*Syncing opportunity for HubspotYour HubSpotaccount has become disconnected. Please login to Jiminny to reconnect. skipping...root@docker_lamp_1:/home/jiminny# php artisan jiminny:token-info -A 1499 -RDEVaccess_tokenCFPAsBNZxoDp5kAcRyeB1QoE5SM7DSgNuYTFSAFoAYABo3tj9DHAAeAACNeR-JHgMxIZQLNQMl8kQEwrAgwACAkUAhIJBB4BAQEDBxiCiYwCIN7Y_Qwo0qwCMhTnG549n-YtNuc1jgj-2AsLPSmw3DoyQLNQM18kQEwrAiUACBkGawEFThwBARIBAQEEATEEAQEBAQEBAQEBAQUBEggBAQEBAY1access_token_expires_at= 2026-05-07 11:41:20refresh_token => d5ab04e2-2109-4c0b-b513-8cba1dd54371refresh_token_expires_at =root@docker_lamp_1:/home/jiminny#...
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-8543218788614548688
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iTerm2ShellEditViewSessionScriptsProfilesWindowHel iTerm2ShellEditViewSessionScriptsProfilesWindowHelp(ah]• Support Daily • in 36 m100% [Thu 7 May 14:25:00DEV (docker)T81DOCKERLast login: Thu MayO ₴1DEV (docker)7 09:44:56 on ttys006H82APP (-zsh)83-zsh• 84screenpipe*•$5-zsh₴6Poetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentsPoetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentslukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-20773-fix-automated-reports-user-pilot-tracking) $ devroot@docker_lamp_1:/home/jiminny# php artisan crm:sync-opportunity --teamId=2 --from='2026-05-01 00:00:00*Syncing opportunity for HubspotYour HubSpotaccount has become disconnected. Please login to Jiminny to reconnect. skipping...root@docker_lamp_1:/home/jiminny# php artisan jiminny:token-info -A 1499 -RDEVaccess_tokenCFPAsBNZxoDp5kAcRyeB1QoE5SM7DSgNuYTFSAFoAYABo3tj9DHAAeAACNeR-JHgMxIZQLNQMl8kQEwrAgwACAkUAhIJBB4BAQEDBxiCiYwCIN7Y_Qwo0qwCMhTnG549n-YtNuc1jgj-2AsLPSmw3DoyQLNQM18kQEwrAiUACBkGawEFThwBARIBAQEEATEEAQEBAQEBAQEBAQUBEggBAQEBAY1access_token_expires_at= 2026-05-07 11:41:20refresh_token => d5ab04e2-2109-4c0b-b513-8cba1dd54371refresh_token_expires_at =root@docker_lamp_1:/home/jiminny#...
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2026-05-07T11:25:00.750763+00:00
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/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778153100750_m2.jpg...
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PostmanFileEditViewWindowHelpHubSpot rate limit im PostmanFileEditViewWindowHelpHubSpot rate limit implementation strategy"results": ["name": "private-apps-api-calls-daily","usageLimit": 1000000,"currentUsage": 0,"collectedAt": "2026-05-07T11:23:01.362Z",40suppont Dally • In 3omQ SearchYour 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.SEARCH › search deals= DocsAuthorization • Headers 11 Body • ScriptsSettingseraw• binary GraphQL JSON~rilters":"value": 1773041124362No environmentv) SaveCookiesSchema Boauiry100% L2VAIlVariables in requestG token› All variablesInu / May 14.20.00UpgradeCNeR-JHgMxIZQINQ...7 Claritving that 1M dailv limit applies onlv to private apos. not OAuthiKeep going in Claude CodeSwitch to Claude Code and let Claude work directlv in vour reno.running and testing as it goes.Write a message…Opus 4.7 AdaptiveClaudo ic Aland can mako mistakes Plesce double-chock racnoncac)v COLLECtIONs› CRM ObjectsCRM Owners> CRM Pipelines> DealsEngagements• D OLD ENGAGEMENTSGET list meetingsPost search tasksGET read call> Post search callsGET list callsPoST meetings scheduledGET aet meetinaPost get link to task> POST Create Contact with Association› HubspotJournal & webhoooks v4POST Get tokenPOST create subscriptionGET Journal earliestGET Journal latestGET https:/ubspot-webhooks-journal-nal.s.GET next offsetPOST aet Token prodDEL DELETE SUBSCRIPTION PER PORTALGET DEAL WITH HISTORY PROPERTIES V3› OAuth› Properties> PESCAPCHV SEARCHIPoST search contact by phonePost search contact ov emailiPOST search meetingsPOST search notes> PosT Search calls v3POST Search related meetings v3>ENVIRONMENTS> SPFCS>FLOWS@ Connect Git = ConcoldE Term"hs_call_direction",BOdVCookiesJSONvPreviewe. Visualize"total": 51"xesults". 1"arontodoto". w2019-06.16707-22-66 20274: 2015-035-16-07 :8218 3327) 3a02' ,"hubsoot owner id". "579583316"2018-05-16107:32:50.3032"updatedAt: 2026-05-04105:33:47.3402"archived": talse."ux": "httos:aop.hubspot.com/contacts/4392066/xecoxd/0-3/297846423'"id". "181281563".#nronortjoc".S"crontodato". 12010-01.22715-21-10 2607"the. aetrodato* 4201- 02-2516-2726-1905.6942,* Crato: 2029-04- 25718:21:69.8692200 OK • 319 ms • 1.7 KB - (à| 5e Save Response ..==aID8...
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2467685805704084309
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PostmanFileEditViewWindowHelpHubSpot rate limit im PostmanFileEditViewWindowHelpHubSpot rate limit implementation strategy"results": ["name": "private-apps-api-calls-daily","usageLimit": 1000000,"currentUsage": 0,"collectedAt": "2026-05-07T11:23:01.362Z",40suppont Dally • In 3omQ SearchYour 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.SEARCH › search deals= DocsAuthorization • Headers 11 Body • ScriptsSettingseraw• binary GraphQL JSON~rilters":"value": 1773041124362No environmentv) SaveCookiesSchema Boauiry100% L2VAIlVariables in requestG token› All variablesInu / May 14.20.00UpgradeCNeR-JHgMxIZQINQ...7 Claritving that 1M dailv limit applies onlv to private apos. not OAuthiKeep going in Claude CodeSwitch to Claude Code and let Claude work directlv in vour reno.running and testing as it goes.Write a message…Opus 4.7 AdaptiveClaudo ic Aland can mako mistakes Plesce double-chock racnoncac)v COLLECtIONs› CRM ObjectsCRM Owners> CRM Pipelines> DealsEngagements• D OLD ENGAGEMENTSGET list meetingsPost search tasksGET read call> Post search callsGET list callsPoST meetings scheduledGET aet meetinaPost get link to task> POST Create Contact with Association› HubspotJournal & webhoooks v4POST Get tokenPOST create subscriptionGET Journal earliestGET Journal latestGET https:/ubspot-webhooks-journal-nal.s.GET next offsetPOST aet Token prodDEL DELETE SUBSCRIPTION PER PORTALGET DEAL WITH HISTORY PROPERTIES V3› OAuth› Properties> PESCAPCHV SEARCHIPoST search contact by phonePost search contact ov emailiPOST search meetingsPOST search notes> PosT Search calls v3POST Search related meetings v3>ENVIRONMENTS> SPFCS>FLOWS@ Connect Git = ConcoldE Term"hs_call_direction",BOdVCookiesJSONvPreviewe. Visualize"total": 51"xesults". 1"arontodoto". w2019-06.16707-22-66 20274: 2015-035-16-07 :8218 3327) 3a02' ,"hubsoot owner id". "579583316"2018-05-16107:32:50.3032"updatedAt: 2026-05-04105:33:47.3402"archived": talse."ux": "httos:aop.hubspot.com/contacts/4392066/xecoxd/0-3/297846423'"id". "181281563".#nronortjoc".S"crontodato". 12010-01.22715-21-10 2607"the. aetrodato* 4201- 02-2516-2726-1905.6942,* Crato: 2029-04- 25718:21:69.8692200 OK • 319 ms • 1.7 KB - (à| 5e Save Response ..==aID8...
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2026-05-07T11:25:02.208955+00:00
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/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778153102208_m2.jpg...
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monitor_2
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PostmanFileEditViewWindowHelpHubSpot rate limit im PostmanFileEditViewWindowHelpHubSpot rate limit implementation strategy v"results":"name": "private-apps-api-calls-daily","usageLimit": 1000000,"currentUsage": 0"collectedAt": "2026-05-07T11:23:01.3627".**- Clarifving that 1M dailv limit applies onlv to private apps. not OAuthl• SearchYour 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.SEARCH › search deals= DocsParamsAuthorization • Headers 11 Body • ScriptsSettinas• torm-datax-www-form-urlencodederaw• binary • GraphQL JSON ~rilters":"operator". "GT""value": 1773041124362'hs call direccion."values": INBOUND", OUTBOUND""suppont Dally • In 3omNo environmentv) SaveCookieso Schema Beautify100% L2VAIlVariables in requestG token› All VarlablesInu / May 14.20.02UparadeCNeR-JHaMxlZoiNd.Keep going in Claude CodeSwitch to Claude Code and let Claude work directlv in vour reno.running and testing as it goes.Write a message…Opus 4.7 Adaptive vClaudo ic Aand can mako mictakas Plesce double-chock racnoncocv COLLECtIONs› CRM ObjectsCRM Owners> CPM Pinelinec> DealsEngagements• D OLD ENGAGEMENTSGET list meetingspost coarch tackeGET read call> PosT search callsGET list callsPOST meetings scheduledGET aet meetinaPOST get link to task> POST Create Contact with Association> HubsnotJournal & webhoooks v4POSt Get tokenPOST create subscrintionGET Journal earliestGET Journal latestGET https:/ubspot-webhooks-journal-nal.s.GET next offsetPOST aet Token prodDEL DELETE SURSCRIPTION DER PORTALIGET DEAL WITH HISTORY PROPERTIES V3› OAuth› Properties> PESCAPCHV SEARCHIPOST search contact by phonePost search contact ov emailliPOST search meetingsPOST search notespost Soarch calle v2POST Search related meetinas v3ENMIDANMENTS> SPFCSELOWS@ Connect Git = Concold2 Termir200 OK • 319 ms • 1.7 KB fe.g. save kesponsedatecontent-typecontent-lenathlct-ravnf-cache-statuscontent-encodingstrict-transport-securityaccess-control-allow-credentialsserver-timinax-content-type-optionsx-hubspot-correlation-idreport-toThu. 07 May 2026 11:25:00 GMTIapplication/json charset=utf-89f7fdc8a7b508428-SOFDYNAMIGmax-age=31536000: includeSubDomains: preloadorigin, accept-encodingfalsehcid:desc="019e022f-12de-744e-9338-bab37cbb56a1", cfr:desc="9f7fdc8a857e3402-IAD"nocniff019e022f-12de-744e-9338-bab37cbb56a1Wondoointe!.fiuel.httnelMa.nolcloudflor..com/eonortWlul0e._40/0FOw7umV7V2EV7.TOpf"suecocs fraction".0 01 "renort to"."cf_nel""may aae".604800}...
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PostmanFileEditViewWindowHelpHubSpot rate limit im PostmanFileEditViewWindowHelpHubSpot rate limit implementation strategy v"results":"name": "private-apps-api-calls-daily","usageLimit": 1000000,"currentUsage": 0"collectedAt": "2026-05-07T11:23:01.3627".**- Clarifving that 1M dailv limit applies onlv to private apps. not OAuthl• SearchYour 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.SEARCH › search deals= DocsParamsAuthorization • Headers 11 Body • ScriptsSettinas• torm-datax-www-form-urlencodederaw• binary • GraphQL JSON ~rilters":"operator". "GT""value": 1773041124362'hs call direccion."values": INBOUND", OUTBOUND""suppont Dally • In 3omNo environmentv) SaveCookieso Schema Beautify100% L2VAIlVariables in requestG token› All VarlablesInu / May 14.20.02UparadeCNeR-JHaMxlZoiNd.Keep going in Claude CodeSwitch to Claude Code and let Claude work directlv in vour reno.running and testing as it goes.Write a message…Opus 4.7 Adaptive vClaudo ic Aand can mako mictakas Plesce double-chock racnoncocv COLLECtIONs› CRM ObjectsCRM Owners> CPM Pinelinec> DealsEngagements• D OLD ENGAGEMENTSGET list meetingspost coarch tackeGET read call> PosT search callsGET list callsPOST meetings scheduledGET aet meetinaPOST get link to task> POST Create Contact with Association> HubsnotJournal & webhoooks v4POSt Get tokenPOST create subscrintionGET Journal earliestGET Journal latestGET https:/ubspot-webhooks-journal-nal.s.GET next offsetPOST aet Token prodDEL DELETE SURSCRIPTION DER PORTALIGET DEAL WITH HISTORY PROPERTIES V3› OAuth› Properties> PESCAPCHV SEARCHIPOST search contact by phonePost search contact ov emailliPOST search meetingsPOST search notespost Soarch calle v2POST Search related meetinas v3ENMIDANMENTS> SPFCSELOWS@ Connect Git = Concold2 Termir200 OK • 319 ms • 1.7 KB fe.g. save kesponsedatecontent-typecontent-lenathlct-ravnf-cache-statuscontent-encodingstrict-transport-securityaccess-control-allow-credentialsserver-timinax-content-type-optionsx-hubspot-correlation-idreport-toThu. 07 May 2026 11:25:00 GMTIapplication/json charset=utf-89f7fdc8a7b508428-SOFDYNAMIGmax-age=31536000: includeSubDomains: preloadorigin, accept-encodingfalsehcid:desc="019e022f-12de-744e-9338-bab37cbb56a1", cfr:desc="9f7fdc8a857e3402-IAD"nocniff019e022f-12de-744e-9338-bab37cbb56a1Wondoointe!.fiuel.httnelMa.nolcloudflor..com/eonortWlul0e._40/0FOw7umV7V2EV7.TOpf"suecocs fraction".0 01 "renort to"."cf_nel""may aae".604800}...
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2026-05-07T11:25:16.598356+00:00
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/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778153116598_m1.jpg...
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iTerm2
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True
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iTerm2ShellEditViewSessionScriptsProfilesWindowHel iTerm2ShellEditViewSessionScriptsProfilesWindowHelp(ah]• Support Daily • in 35 m100%8DEV (docker)83Thu 7 May 14:25:16181₴6DOCKERLast login: Thu MayDEV (docker)H82APP (-zsh)-zsh• 84screenpipe*•$5-zsh7 09:44:56 on ttys006Poetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentsPoetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentslukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-20773-fix-automated-reports-user-pilot-tracking) $ devroot@docker_lamp_1:/home/jiminny# php artisan crm:sync-opportunity --teamId=2 --from='2026-05-01 00:00:00*Syncing opportunity for HubspotYour HubSpotaccount has become disconnected. Please login to Jiminny to reconnect. skipping...root@docker_lamp_1:/home/jiminny# php artisan jiminny:token-info -A 1499 -RDEVaccess_tokenCFPAsBNZxoDp5kAcRyeB1QoE5SM7DSgNuYTFSAFoAYABo3tj9DHAAeAACNeR-JHgMxIZQLNQMl8kQEwrAgwACAkUAhIJBB4BAQEDBxiCiYwCIN7Y_Qwo0qwCMhTnG549n-YtNuc1jgj-2AsLPSmw3DoyQLNQM18kQEwrAiUACBkGawEFThwBARIBAQEEATEEAQEBAQEBAQEBAQUBEggBAQEBAY1access_token_expires_at= 2026-05-07 11:41:20refresh_token => d5ab04e2-2109-4c0b-b513-8cba1dd54371refresh_token_expires_at =root@docker_lamp_1:/home/jiminny#...
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iTerm2ShellEditViewSessionScriptsProfilesWindowHel iTerm2ShellEditViewSessionScriptsProfilesWindowHelp(ah]• Support Daily • in 35 m100%8DEV (docker)83Thu 7 May 14:25:16181₴6DOCKERLast login: Thu MayDEV (docker)H82APP (-zsh)-zsh• 84screenpipe*•$5-zsh7 09:44:56 on ttys006Poetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentsPoetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentslukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-20773-fix-automated-reports-user-pilot-tracking) $ devroot@docker_lamp_1:/home/jiminny# php artisan crm:sync-opportunity --teamId=2 --from='2026-05-01 00:00:00*Syncing opportunity for HubspotYour HubSpotaccount has become disconnected. Please login to Jiminny to reconnect. skipping...root@docker_lamp_1:/home/jiminny# php artisan jiminny:token-info -A 1499 -RDEVaccess_tokenCFPAsBNZxoDp5kAcRyeB1QoE5SM7DSgNuYTFSAFoAYABo3tj9DHAAeAACNeR-JHgMxIZQLNQMl8kQEwrAgwACAkUAhIJBB4BAQEDBxiCiYwCIN7Y_Qwo0qwCMhTnG549n-YtNuc1jgj-2AsLPSmw3DoyQLNQM18kQEwrAiUACBkGawEFThwBARIBAQEEATEEAQEBAQEBAQEBAQUBEggBAQEBAY1access_token_expires_at= 2026-05-07 11:41:20refresh_token => d5ab04e2-2109-4c0b-b513-8cba1dd54371refresh_token_expires_at =root@docker_lamp_1:/home/jiminny#...
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2026-05-07T11:25:16.598300+00:00
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/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778153116598_m2.jpg...
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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 AdaptiveQ SearchYour 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 RejSEARCH › search deals= DocsParamsAuthorization • Headers 11 Body • ScriptsSettinas• torm-dataeraw• binary • GraphQL JSON ~rilters":"operator". "GT""value": 17730411243623092055806"hs call direction,VaLues": ["INBOUND", "OUTBOUND"7"suppont Dally • In 3omNo environmentv) SaveCookieso Schema Beautify100% L2VAIlVariables in requestG token› All VarlablesInu / May 14.20.10UparadeCNeR-JHaMxlZoiNd.v COLLECtIONs> CRM ObjectsCRM Owners> CPM Pinelinec> DealsEngagements• D OLD ENGAGEMENTSGET list meetingspost coarch tackeGET read callPOST search callsGET list callsPOST meetings scheduledGET aet meetinaPOST get link to task> POST Create Contact with Association> HubsnotJournal & webhoooks v4POSt Get tokenPOST create subscrintionGET Journal earliestGET Journal latestGET https:/ubspot-webhooks-journal-nal.s.GET next offsetPOST aet Token prodDEL DELETE SURSCRIPTION DER PORTAL.GET DEAL WITH HISTORY PROPERTIES V3> OAuth› Properties> PESCAPCHV SEARCHIPOST search contact by phonePost search contact ov emailliPOST search meetingsPOST search notespost Soarch calle v2POST Search related meetinas v3ENMIDANMENTS> SPFCSELOWS@ Connect Git = ConcoldTern200 OK • 319 ms • 1.7 KB fe.g. save kesponsedatecontent-typecontent-lenathlct-ravnf-cache-statuscontent-encodingstrict-transport-securityaccess-control-allow-credentialsserver-timinax-content-type-optionsx-hubspot-correlation-idreport-toThu, 07 May 2026 11:25:00 GMTIapplication/json charset=utf-89f7fdc8a7b508428-SOFDYNAMIGmax-age=31536000: includeSubDomains: preloadorigin, accept-encodingfalsehcid:desc="019e022f-12de-744e-9338-bab37cbb56a1", cfr:desc="9f7fdc8a857e3402-IAD"nocniff019e022f-12de-744e-9338-bab37cbb56a1Wondoointe!.fiuel.httnelMa.nolcloudflor..com/eonortWlul0e._40/0FOw7umV7V2EV7.TOpf"suecocs fraction".0.01 "renort to"."cf-ne|""may aae".604800}...
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-2632456244707685288
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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 AdaptiveQ SearchYour 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 RejSEARCH › search deals= DocsParamsAuthorization • Headers 11 Body • ScriptsSettinas• torm-dataeraw• binary • GraphQL JSON ~rilters":"operator". "GT""value": 17730411243623092055806"hs call direction,VaLues": ["INBOUND", "OUTBOUND"7"suppont Dally • In 3omNo environmentv) SaveCookieso Schema Beautify100% L2VAIlVariables in requestG token› All VarlablesInu / May 14.20.10UparadeCNeR-JHaMxlZoiNd.v COLLECtIONs> CRM ObjectsCRM Owners> CPM Pinelinec> DealsEngagements• D OLD ENGAGEMENTSGET list meetingspost coarch tackeGET read callPOST search callsGET list callsPOST meetings scheduledGET aet meetinaPOST get link to task> POST Create Contact with Association> HubsnotJournal & webhoooks v4POSt Get tokenPOST create subscrintionGET Journal earliestGET Journal latestGET https:/ubspot-webhooks-journal-nal.s.GET next offsetPOST aet Token prodDEL DELETE SURSCRIPTION DER PORTAL.GET DEAL WITH HISTORY PROPERTIES V3> OAuth› Properties> PESCAPCHV SEARCHIPOST search contact by phonePost search contact ov emailliPOST search meetingsPOST search notespost Soarch calle v2POST Search related meetinas v3ENMIDANMENTS> SPFCSELOWS@ Connect Git = ConcoldTern200 OK • 319 ms • 1.7 KB fe.g. save kesponsedatecontent-typecontent-lenathlct-ravnf-cache-statuscontent-encodingstrict-transport-securityaccess-control-allow-credentialsserver-timinax-content-type-optionsx-hubspot-correlation-idreport-toThu, 07 May 2026 11:25:00 GMTIapplication/json charset=utf-89f7fdc8a7b508428-SOFDYNAMIGmax-age=31536000: includeSubDomains: preloadorigin, accept-encodingfalsehcid:desc="019e022f-12de-744e-9338-bab37cbb56a1", cfr:desc="9f7fdc8a857e3402-IAD"nocniff019e022f-12de-744e-9338-bab37cbb56a1Wondoointe!.fiuel.httnelMa.nolcloudflor..com/eonortWlul0e._40/0FOw7umV7V2EV7.TOpf"suecocs fraction".0.01 "renort to"."cf-ne|""may aae".604800}...
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2026-05-07T11:25:18.561766+00:00
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/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778153118561_m2.jpg...
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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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8861534449038836230
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visual_change
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ocr
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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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