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PostmanEditVIewWindowmelpHubSpot rate limit imple PostmanEditVIewWindowmelpHubSpot rate limit implementation strategyX-HubSpot-RateLimit-Secondly-RemainingDeprecated, ignoreSO Max=190, Interval=10000 means this portal is on a Pro/ Enterprise privately-distributedaoo aliyuius. I vousee max=110 .rs a oubnc VAutn nstal. max=250 means they nave uneAPI Limit Increase add-on.What vou cannot get via API• Search 5/sec limit - not exposed anywhere. Not in headers (search responses stripthem), not in any endpoint. It's a documented constant; vou assume it.• Daily usage broken out by app within a portal — the daily endpoint aggregates all privateapps. You can't tell from the API which app spent the budget.• Per-app burst limit programmatically — only inferred from Max in headers from a callthat app madePostman recide to fullv profile a vortallIhree calls in order.1. GET /account-info/v3/details →grab portalld. timeZone also notethe x-HunSnot-Rate imit-x resnonse neaders (this is vour burst nronle)2. GET /account-info/v3/api-usage/daily/private-apps → daily limit, current spend.reset time3. (Optional) Trigger a 429 deliberately on a sandbox to confirm policyName shape, but thisisn't necessary just for inspection.That gives vou evervthing HubSpot will tell vou about a specific portal's limits. Save the tworequests as a Postman collection with (faccess token}} and ((portal idi} ascollectionvariables and vou can profile anv portal in two clicks.Keep going in Claude CodeSwitch to Claude Code and let Claude work directiv in vour reno..running and testing as it goesWrite a message…Opus 4.7 Adaptive v0 hllQ 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.https://lapi.hubapi.com/account-info/v3/api-usaqe/daily/private-appshttps://api.hubapi.com/account-info/v3/api-usage/daily/private-apps= DocsParamsAuthorization • Headers 9 Body ScriptsSettinasAuth TypeTokenThe authorization header will be automaticallygenerated when you send the reauest. Learnmore about Bearer Token authorizationGET httos://:{tokenl}"supoont Dally • In 47m100% L2Inu / May 14:13-20No environmentva SaveSeng* ~ Cookies4* AIVariables in requestG token› All Varlablesv COLLECtIONscontacts› CRM ObjectseRM owners> CRM Pipelines> DealsEngagements• D OLD ENGAGEMENTSGET list meetingsPST search moditied companiePOST search tasksGET read call> Post search callsGET list callsPOST meetings scheduledGET aet meetinaPOST get link to task> POST Create Contact with Associatio> HubsnotJournal & webhoooks v4POSt Get tokenGET det subscriptionsPOST create subscrintionGET Journal earliestGET lournal latestGeT https:ubspot-webhooks-lournal-nal.s.GET neyt offcotpost get loken prodDEL DELSTE CURSCRIPTION DEP PORTAI.GET DEAL WITH HISTORY pRODEpTICC VA©Authi> Properties> RESSARCHwCCADAUPOSt search contact bv ohonePOSt search contact by emailPOST search meetinasPOST search notes> Post Search calls v3.IPOST Search related meetinas v3POST search dealsCAMIDONMCNTC> SPFCSELOWS$ Connect Git @ Console TermirCNeR-JHaMxlZoiNd.BOdVJSONv200 OK • 591 ms • 1.23 KB •(| .•CQ108Previewp. Visualize v"portalld": 4392066odditionalCurroncioch. ПGlobals Vault Tools?000...
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PostmanEditVIewWindowmelpHubSpot rate limit imple PostmanEditVIewWindowmelpHubSpot rate limit implementation strategyX-HubSpot-RateLimit-Secondly-RemainingDeprecated, ignoreSO Max=190, Interval=10000 means this portal is on a Pro/ Enterprise privately-distributedaoo aliyuius. I vousee max=110 .rs a oubnc VAutn nstal. max=250 means they nave uneAPI Limit Increase add-on.What vou cannot get via API• Search 5/sec limit - not exposed anywhere. Not in headers (search responses stripthem), not in any endpoint. It's a documented constant; vou assume it.• Daily usage broken out by app within a portal — the daily endpoint aggregates all privateapps. You can't tell from the API which app spent the budget.• Per-app burst limit programmatically — only inferred from Max in headers from a callthat app madePostman recide to fullv profile a vortallIhree calls in order.1. GET /account-info/v3/details →grab portalld. timeZone also notethe x-HunSnot-Rate imit-x resnonse neaders (this is vour burst nronle)2. GET /account-info/v3/api-usage/daily/private-apps → daily limit, current spend.reset time3. (Optional) Trigger a 429 deliberately on a sandbox to confirm policyName shape, but thisisn't necessary just for inspection.That gives vou evervthing HubSpot will tell vou about a specific portal's limits. Save the tworequests as a Postman collection with (faccess token}} and ((portal idi} ascollectionvariables and vou can profile anv portal in two clicks.Keep going in Claude CodeSwitch to Claude Code and let Claude work directiv in vour reno..running and testing as it goesWrite a message…Opus 4.7 Adaptive v0 hllQ 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.https://lapi.hubapi.com/account-info/v3/api-usaqe/daily/private-appshttps://api.hubapi.com/account-info/v3/api-usage/daily/private-apps= DocsParamsAuthorization • Headers 9 Body ScriptsSettinasAuth TypeTokenThe authorization header will be automaticallygenerated when you send the reauest. Learnmore about Bearer Token authorizationGET httos://:{tokenl}"supoont Dally • In 47m100% L2Inu / May 14:13-20No environmentva SaveSeng* ~ Cookies4* AIVariables in requestG token› All Varlablesv COLLECtIONscontacts› CRM ObjectseRM owners> CRM Pipelines> DealsEngagements• D OLD ENGAGEMENTSGET list meetingsPST search moditied companiePOST search tasksGET read call> Post search callsGET list callsPOST meetings scheduledGET aet meetinaPOST get link to task> POST Create Contact with Associatio> HubsnotJournal & webhoooks v4POSt Get tokenGET det subscriptionsPOST create subscrintionGET Journal earliestGET lournal latestGeT https:ubspot-webhooks-lournal-nal.s.GET neyt offcotpost get loken prodDEL DELSTE CURSCRIPTION DEP PORTAI.GET DEAL WITH HISTORY pRODEpTICC VA©Authi> Properties> RESSARCHwCCADAUPOSt search contact bv ohonePOSt search contact by emailPOST search meetinasPOST search notes> Post Search calls v3.IPOST Search related meetinas v3POST search dealsCAMIDONMCNTC> SPFCSELOWS$ Connect Git @ Console TermirCNeR-JHaMxlZoiNd.BOdVJSONv200 OK • 591 ms • 1.23 KB •(| .•CQ108Previewp. Visualize v"portalld": 4392066odditionalCurroncioch. ПGlobals Vault Tools?000...
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/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778152424698_m2.jpg...
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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
Show more
10:42
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Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
Copy
Give positive feedback
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Retry
You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
Retry
Edit
Copy
Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
Copy to clipboard
php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.
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Give positive feedback
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Retry
You said: Ok, so what will happen each time I make a request?
You said: Ok, so what will happen each time I make a request?
Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?
13:44
Retry
Edit
Copy
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Analyzed per-request overhead concerns and quantified Redis performance impact
Analyzed per-request overhead concerns and quantified Redis performance impact...
|
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Think carefully about the implementation and potential issue and bottlenecks.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Show more","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"10:42","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Edit","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"Claude responded: Current HubSpot limits (Apr 2026)","depth":21,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Claude responded: Current HubSpot limits (Apr 2026)","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Current HubSpot limits (Apr 2026)","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Current HubSpot limits (Apr 2026)","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". The burst window is per app; the daily quota is per portal.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". That's the limit the 429 in your example is hitting (","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"secondly limit","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"on","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/crm/v3/objects/contact/search","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"). It's a separate bucket from the 190/10s, and critically, search responses do not include the","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-*","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"headers","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— you can't read remaining capacity off the response, you have to track it yourself.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Non-search endpoints return","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Max","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", and","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Interval-Milliseconds","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". The daily headers are not returned for OAuth-authenticated calls","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— that's enforced for marketplace listing, but it's also a sane operational target.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"So you have","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"four buckets","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Where to store it","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Where to store it","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"INCR","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"EXPIRE","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"plus Lua scripting for true CAS semantics.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Key layout I'd use:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:search:{portalId} ZSET (sliding 1s)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:meta:{portalId} HASH (last seen headers)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The two short-window buckets are sliding-window-log (Redis ZSET, score =","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"microtime","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The daily bucket is a plain","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"INCR","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with TTL set to seconds-until-midnight in","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"HubSpot's account timezone","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to read it","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to read it","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Two-phase: optimistic pre-check, authoritative post-update.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Before a call, run the Lua acquire script. If it returns \"denied,\" sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"After the call, parse","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Max","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Interval-Milliseconds","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and store them in","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:meta:{portalId}","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"For search specifically, since headers don't come back, the local counter","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"is","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"not","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"on 429 (you really did make that request).","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to work with multiple jobs","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to work with multiple jobs","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Concurrency cap","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"via","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Queue::throttle()","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Per-tenant fairness","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Priority lanes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot-priority","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot-bulk","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Backoff on 429","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— respect the","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry-After","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"header. If absent, exponential with jitter (e.g.,","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"min(2^attempt * 250ms, 30s) ± 20%","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to work with paginated requests","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to work with paginated requests","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The trap is treating \"fetch all\" as one logical operation. Each page is its own API call and competes for tokens with everything else.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Two practical rules:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Don't hold the worker idle while paginating.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"processing","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"of page N as a separate job, and queue a \"fetch page N+1\" job with a delay equal to the wait time. Each page becomes its own atomic unit.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Always batch where HubSpot offers it.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/{type}/batch/{read|update|create}","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"accepts up to 100 IDs per call. For your \"patching IDs\" flow this is the difference between 600 calls and 6.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Walkthrough: 600 opportunities","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Walkthrough: 600 opportunities","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Sane flow:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Identify deltas","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— if you can avoid Search at all, do. Use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Batch read","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"current state —","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/deals/batch/read","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with 100 IDs per call → 6 calls. Burst weight 6.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Compute diff","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"in your code (no API).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Batch update","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"—","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/deals/batch/update","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with 100 per call → 6 calls.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Associations","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"—","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v4/associations/{from}/{to}/batch/create","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"if needed → another few batch calls.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Recognition: pre-check denies if","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"<weight>","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"slots aren't free; on 429 the response surfaces","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"policyName","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(DAILY/SECONDLY) — log it, push the job back with","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry-After","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"delay, increment a metric so you can alarm on sustained 429 rate.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Pitfalls & bottlenecks worth pre-empting","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Pitfalls & bottlenecks worth pre-empting","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Daily quota is shared across the whole portal","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search is uniquely fragile","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":": separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hs_object_id","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"ranges or by","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"windows.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry storms after HubSpot incidents","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Webhook → sync → webhook loops","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"jy_last_sync_id","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":") and ignore webhooks where it just changed.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Token refresh","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"expires_in - 60s","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", single-flight the refresh (only one worker refreshes; others wait on a lock).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search latency for fresh writes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Clock skew","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— Redis is authoritative. Don't use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"time()","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"from each worker for window scoring; use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"redis.call('TIME')","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"inside the Lua script.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Dead workers with held tokens","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The 5% error rule","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Phantom contention with Laravel","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"RateLimited","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"middleware","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":": it's per-job-class by default, not per-portal. If your","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"SyncDealJob","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"UpdateActivityJob","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"HubspotRateLimiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"service that everything routes through.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give positive feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give negative feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"You said: What will happen if there are two different types of rate limits?","depth":20,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"You said: What will happen if there are two different types of rate limits?","depth":21,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"What will happen if there are two different types of rate limits?","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search requests that have 5 requests per second","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"13:40","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Edit","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"Claude responded: They're independent buckets — with one shared bucket on top.","depth":21,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Claude responded: They're independent buckets — with one shared bucket on top.","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Analyzed independent rate limit buckets and their practical interactions","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Analyzed independent rate limit buckets and their practical interactions","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"They're independent buckets — with one shared bucket on top.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"The three buckets and what each call hits","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"The three buckets and what each call hits","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"┌──────────────────┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search request ──→ │ Search: 5/sec │ ──┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"└──────────────────┘ │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"├──→ ┌─────────────────┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"┌──────────────────┐ │ │ Daily: 250k–1M │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"└──────────────────┘ └─────────────────┘","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"What this means practically","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"What this means practically","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting search secondly limit (5/s):","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Only further","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/search","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting burst limit (190/10s):","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting daily limit:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"So can one type of request block the other?","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"So can one type of request block the other?","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search → Burst:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"No.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Burst → Search:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"No.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Saturating burst leaves Search's 5/s untouched.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Either → Daily:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Yes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", but only by contributing to the same daily counter. It's volume that kills you here, not the type.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"In theory you can sustain ~50 search + ~190 other =","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"~240 calls per 10 seconds","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"indefinitely (until daily runs out), because the two buckets bill independently.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Implementation consequence","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Implementation consequence","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"php","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'search'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// for search endpoints only","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'burst'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// for everything else","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'daily'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// every call, both kinds","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('search')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"+","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('daily')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". A batch update calls","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('burst')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"+","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('daily')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":".","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"One subtle pitfall: when you backoff on a 429,","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"inspect","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"policyName","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"in the response body","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to know which bucket actually fired. If you see","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"SECONDLY","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The other subtlety: the burst response gives you","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give positive feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give negative feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"You said: Ok, so what will happen each time I make a request?","depth":20,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"You said: Ok, so what will happen each time I make a request?","depth":21,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"13:44","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Edit","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.","depth":21,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Analyzed per-request overhead concerns and quantified Redis performance impact","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Analyzed per-request overhead concerns and quantified Redis performance impact","depth":23,"on_screen":false,"role_description":"text"}]...
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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
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10:42
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Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
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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...
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/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778152425508_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
]
)
or
'0'
)
if
burst_used
>=
tonumber
(
ARGV
[
3
]
)
then
-- Tell caller how long to sleep until oldest entry expires
local
oldest
=
redis
.
call
(
'ZRANGE'
,
KEYS
[
1
]
,
0
,
0
,
'WITHSCORES'
)
return
{
0
,
'BURST'
,
(
oldest
[
2
]
+
ARGV
[
2
]
)
-
ARGV
[
1
]
}
end
if
daily_used
>=
tonumber
(
ARGV
[
4
]
)
then
return...
|
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The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:","depth":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_descriptio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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 finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
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Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
Copy
Give positive feedback
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Retry
You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
Retry
Edit
Copy
Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
Copy to clipboard
php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.
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Retry
You said: Ok, so what will happen each time I make a request?
You said: Ok, so what will happen each time I make a request?
Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?
13:44
Retry
Edit
Copy
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Analyzed per-request overhead concerns and quantified Redis performance impact
Analyzed per-request overhead concerns and quantified Redis performance impact
Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out. But the overhead is much smaller than it sounds, and importantly, it's nowhere near your bottleneck.
What "two buckets per request" actually costs
What "two buckets per request" actually costs
Each request makes
one round trip to Redis
— not two — because you fold both bucket checks into a single Lua script that runs atomically server-side:
Copy to clipboard
lua
-- KEYS[1] = burst bucket key (or search)
-- KEYS[2] = daily bucket key
-- ARGV[1] = now (ms) ARGV[2] = window_ms
-- ARGV[3] = bucket_max ARGV[4] = daily_max
-- ARGV[5] = request_id ARGV[6] = daily_ttl
-- Trim sliding window
redis
.
call
(
'ZREMRANGEBYSCORE'
,
KEYS
[
1
]
,
0
,
ARGV
[
1
]
-
ARGV
[
2
]
)
local
burst_used
=
redis
.
call
(
'ZCARD'
,
KEYS
[
1
]
)
local
daily_used
=
tonumber
(
redis
.
call
(
'GET'
,
KEYS
[
2
]
)
or
'0'
)
if
burst_used
>=
tonumber
(
ARGV
[
3
]
)
then
-- Tell caller how long to sleep until oldest entry expires
local
oldest
=
redis
.
call
(
'ZRANGE'
,
KEYS
[
1
]
,
0
,
0
,
'WITHSCORES'
)
return
{
0
,
'BURST'
,
(
oldest
[
2
]
+
ARGV
[
2
]
)
-
ARGV
[
1
]
}
end
if
daily_used
>=
tonumber
(
ARGV
[
4
]
)
then
return...
|
NULL
|
NULL
|
NULL
|
NULL
|
|
2377
|
102
|
50
|
2026-05-07T11:13:51.675316+00:00
|
/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778152431675_m2.jpg...
|
Claude
|
Claude
|
True
|
NULL
|
monitor_2
|
NULL
|
NULL
|
NULL
|
NULL
|
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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
Show more
10:42
Retry
Edit
Copy
Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
Copy
Give positive feedback
Give negative feedback
Retry
You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
Retry
Edit
Copy
Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
Copy to clipboard
php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.
Copy
Give positive feedback
Give negative feedback
Retry
You said: Ok, so what will happen each time I make a request?
You said: Ok, so what will happen each time I make a request?
Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?
13:44
Retry
Edit
Copy
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Analyzed per-request overhead concerns and quantified Redis performance impact
Analyzed per-request overhead concerns and quantified Redis performance impact
Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out. But the overhead is much smaller than it sounds, and importantly, it's nowhere near your bottleneck.
What "two buckets per request" actually costs
What "two buckets per request" actually costs
Each request makes
one round trip to Redis
— not two — because you fold both bucket checks into a single Lua script that runs atomically server-side:
Copy to clipboard
lua
-- KEYS[1] = burst bucket key (or search)
-- KEYS[2] = daily bucket key
-- ARGV[1] = now (ms) ARGV[2] = window_ms
-- ARGV[3] = bucket_max ARGV[4] = daily_max
-- ARGV[5] = request_id ARGV[6] = daily_ttl
-- Trim sliding window
redis
.
call
(
'ZREMRANGEBYSCORE'
,
KEYS
[
1
]
,
0
,
ARGV
[
1
]
-
ARGV
[
2
]
)
local
burst_used
=
redis
.
call
(
'ZCARD'
,
KEYS
[
1
]
)
local
daily_used
=
tonumber
(
redis
.
call
(
'GET'
,
KEYS
[
2
]
)
or
'0'
)
if
burst_used
>=
tonumber
(
ARGV
[
3
]
)
then
-- Tell caller how long to sleep until oldest entry expires
local
oldest
=
redis
.
call
(
'ZRANGE'
,
KEYS
[
1
]
,
0
,
0
,
'WITHSCORES'
)
return
{
0
,
'BURST'
,
(
oldest
[
2
]
+
ARGV
[
2
]
)
-
ARGV
[
1
]
}
end
if
daily_used
>=
tonumber
(
ARGV
[
4
]
)
then
return
{
0
,
'DAILY'
,
-
1
}
end
redis
.
call
(
'ZADD'
,
KEYS
[
1
]
,
ARGV
[
1
]
,
ARGV
[
5
]
)
redis
.
call
(
'PEXPIRE'
,
KEYS
[
1
]
,
ARGV
[
2
]
+
1000
)
local
d
=
redis
.
call
(
'INCR'
,
KEYS
[
2
]
)
if
d
==
1
then
redis
.
call
(
'EXPIRE'
,
KEYS
[
2
]
,
ARGV
[
6
]
)
end
return
{
1
,
'OK'
,
tonumber
(
ARGV
[
3
]
)
-
burst_used
-
1
}
One
EVALSHA
call. Two keys touched. Returns either
{1, OK, remaining}
or
{0, reason, retry_ms}
. No race conditions because Lua is single-threaded inside Redis. No "check then increment" gap that other workers can sneak through.
The math on whether this is heavy
The math on whether this is heavy
For your 100,000 requests, with 100 PATCHes batched into 1 batch_update call per 100 deals, you're realistically making more like
1,000–2,000 actual API calls
(assuming you're using
/batch/update
and
/batch/read
properly). But let's pretend you really make all 100k.
At HubSpot's ceiling of ~240 calls/sec across both buckets, 100k calls take roughly
7 minutes
of wall time. That means:
Redis ops/sec:
~240. A laptop-grade Redis instance handles 50,000+. You're using under half a percent.
Per-request overhead:
~0.3–1.0 ms for an
EVALSHA
against local-network Redis.
Per HubSpot call:
80–400 ms typical latency.
So the limiter adds something like
0.3% to each request's wall time
. The rate limit itself is forcing you to wait 4–40 ms between calls anyway; adding 0.5 ms of bookkeeping is invisible.
This is a pattern worth internalizing:
the rate limiter cannot be the bottleneck
, because the thing it's gating (HubSpot itself) is two to three orders of magnitude slower than the gating mechanism.
Things that would make it heavy (and how to avoid them)
Things that would make it heavy (and how to avoid them)
Multiple round trips per request
— if you
GET
then
INCR
then
ZADD
separately, you've tripled the latency and broken atomicity. Use Lua. One trip.
No connection pooling
— opening a TCP connection to Redis per request is 10× more expensive than the Redis op itself. Laravel's Redis facade pools by default; just don't fight it.
Persisting accounting to MySQL
— DB writes per API call would absolutely melt under 240/sec. Redis only. If you want analytics, sample or aggregate after the fact.
Synchronous polling waits
— if a worker is denied and busy-loops calling
acquire
every 10ms, you're putting 100/sec of pressure on Redis per worker for nothing. Either sleep for the
retry_ms
value the script returned, or push the job back to the queue with a delay and let the worker pick up other work.
Per-worker counters that you reconcile
— don't. Single shared Redis counter, period.
The real optimization isn't the limiter
The real optimization isn't the limiter
If you're actually facing 100k requests, the lever isn't shaving microseconds off the limiter — it's
collapsing 100 single-record calls into 1 batch call
. HubSpot's batch endpoints accept up to 100 records per call for read/create/update on most CRM objects:
100,000 single PATCHes → 1,000 batch updates.
100× reduction.
Each search call already returns up to 200 records via
limit: 200
.
Engagement updates (calls/meetings/notes) likewise have batch endpoints.
That single change takes you from 7 minutes against rate limits to under a minute, and drops your daily quota consumption by two orders of magnitude. Redis bookkeeping cost was never the problem; HubSpot's per-call cost was.
So to directly answer the worry: yes, two bucket touches per request, but it's one Redis round trip, sub-millisecond, and your infrastructure load profile for 100k requests is basically "Redis takes a nap while HubSpot rate-limits you."
Copy
Give positive feedback
Give negative feedback
Retry
You said: Can I see rate limits per tenant (portal) in hubspot
You said: Can I see rate limits per tenant (portal) in hubspot
Can I see rate limits per tenant (portal) in hubspot
13:57
Retry
Edit
Copy
Claude responded: Yes, and there are four different sources depending on what kind of view you want.
Claude responded: Yes, and there are four different sources depending on what kind of view you want.
Synthesized multiple HubSpot visibility methods for per-portal rate tracking
Synthesized multiple HubSpot visibility methods for per-portal rate tracking
Yes, and there are four different sources depending on what kind of view you want.
1. Response headers (per-call, real-time, per-portal automatically)
1. Response headers (per-call, real-time, per-portal automatically)
Every non-search response includes the burst-window headers, and they're already per-portal because the OAuth token in the request scopes them to that portal.
X-HubSpot-RateLimit-Max
,
X-HubSpot-RateLimit-Remaining
, and
X-HubSpot-RateLimit-Interval-Milliseconds
come back on every request
. Just read them off and store keyed by
portalId
. This is the cheapest, most accurate signal you can get — straight from HubSpot, no extra calls.
hubspot
hubspot
Two caveats:
For OAuth-authorized requests, the daily headers (
X-HubSpot-RateLimit-Daily
and
-Daily-Remaining...
|
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chat","depth":21,"bounds":{"left":0.48537233,"top":0.026336791,"width":0.010638298,"height":0.025538707},"on_screen":true,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Claude finished the response","depth":21,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"You said: So just a solution for rate limit implementation.","depth":20,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"You said: So just a solution for rate limit implementation.","depth":21,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"updating activity (engagement, meeting, call, note)","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"searching for a company or contact","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"updating deal","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"patching IDs\nReview what the limit is at the moment in the HubSpot documentation and then think of the solution:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"where to store it","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"how to read it","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"how to work with multiple jobs","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines","depth":25,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"). Example of such a error Client error:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST https://api.hubapi.com/crm/v3/objects/contact/search","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"resulted in a","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"429 Too Many Requests","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"response: {\"status\":\"error\",\"message\":\"You have reached your secondly limit.\",\"errorType\":\"RATE_LIMIT\",\"correlationId\":\"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Show more","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"10:42","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"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. Don't busy-loop.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"After the call, parse","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Max","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Interval-Milliseconds","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and store them in","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:meta:{portalId}","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"For search specifically, since headers don't come back, the local counter","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"is","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"not","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"on 429 (you really did make that request).","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to work with multiple jobs","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to work with multiple jobs","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Concurrency cap","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"via","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Queue::throttle()","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Per-tenant fairness","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Priority lanes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot-priority","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot-bulk","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Backoff on 429","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— respect the","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry-After","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"header. 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Each page is its own API call and competes for tokens with everything else.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Two practical rules:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Don't hold the worker idle while paginating.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"processing","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"of page N as a separate job, and queue a \"fetch page N+1\" job with a delay equal to the wait time. Each page becomes its own atomic unit.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Always batch where HubSpot offers it.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/{type}/batch/{read|update|create}","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"accepts up to 100 IDs per call. For your \"patching IDs\" flow this is the difference between 600 calls and 6.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Walkthrough: 600 opportunities","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Walkthrough: 600 opportunities","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Sane flow:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Identify deltas","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— if you can avoid Search at all, do. Use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Batch read","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"current state —","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/deals/batch/read","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with 100 IDs per call → 6 calls. Burst weight 6.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Compute diff","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"in your code (no API).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Batch update","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"—","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/deals/batch/update","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with 100 per call → 6 calls.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Associations","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"—","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v4/associations/{from}/{to}/batch/create","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"if needed → another few batch calls.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Recognition: pre-check denies if","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"<weight>","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"slots aren't free; on 429 the response surfaces","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"policyName","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(DAILY/SECONDLY) — log it, push the job back with","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry-After","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"delay, increment a metric so you can alarm on sustained 429 rate.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Pitfalls & bottlenecks worth pre-empting","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Pitfalls & bottlenecks worth pre-empting","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Daily quota is shared across the whole portal","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search is uniquely fragile","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":": separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hs_object_id","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"ranges or by","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"windows.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry storms after HubSpot incidents","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Webhook → sync → webhook loops","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"jy_last_sync_id","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":") and ignore webhooks where it just changed.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Token refresh","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"expires_in - 60s","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", single-flight the refresh (only one worker refreshes; others wait on a lock).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search latency for fresh writes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Clock skew","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— Redis is authoritative. Don't use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"time()","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"from each worker for window scoring; use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"redis.call('TIME')","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"inside the Lua script.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Dead workers with held tokens","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The 5% error rule","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Phantom contention with Laravel","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"RateLimited","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"middleware","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":": it's per-job-class by default, not per-portal. If your","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"SyncDealJob","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"UpdateActivityJob","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"HubspotRateLimiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"service that everything routes through.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give positive feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give negative feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"You said: What will happen if there are two different types of rate limits?","depth":20,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"You said: What will happen if there are two different types of rate limits?","depth":21,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"What will happen if there are two different types of rate limits?","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search requests that have 5 requests per second","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"13:40","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Edit","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"Claude responded: They're independent buckets — with one shared bucket on top.","depth":21,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Claude responded: They're independent buckets — with one shared bucket on top.","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Analyzed independent rate limit buckets and their practical interactions","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Analyzed independent rate limit buckets and their practical interactions","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"They're independent buckets — with one shared bucket on top.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"The three buckets and what each call hits","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"The three buckets and what each call hits","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"┌──────────────────┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search request ──→ │ Search: 5/sec │ ──┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"└──────────────────┘ │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"├──→ ┌─────────────────┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"┌──────────────────┐ │ │ Daily: 250k–1M │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"└──────────────────┘ └─────────────────┘","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"What this means practically","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"What this means practically","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting search secondly limit (5/s):","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Only further","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/search","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting burst limit (190/10s):","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting daily limit:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"So can one type of request block the other?","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"So can one type of request block the other?","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search → Burst:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"No.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Burst → Search:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"No.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Saturating burst leaves Search's 5/s untouched.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Either → Daily:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Yes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", but only by contributing to the same daily counter. It's volume that kills you here, not the type.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"In theory you can sustain ~50 search + ~190 other =","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"~240 calls per 10 seconds","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"indefinitely (until daily runs out), because the two buckets bill independently.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Implementation consequence","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Implementation consequence","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"php","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'search'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// for search endpoints only","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'burst'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// for everything else","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'daily'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// every call, both kinds","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('search')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"+","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('daily')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". A batch update calls","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('burst')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"+","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('daily')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":".","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"One subtle pitfall: when you backoff on a 429,","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"inspect","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"policyName","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"in the response body","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to know which bucket actually fired. If you see","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"SECONDLY","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The other subtlety: the burst response gives you","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give positive feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give negative feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"You said: Ok, so what will happen each time I make a request?","depth":20,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"You said: Ok, so what will happen each time I make a request?","depth":21,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? 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Just read them off and store keyed by","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"portalId","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". This is the cheapest, most accurate signal you can get — straight from HubSpot, no extra calls.","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":"AXStaticText","text":"Two caveats:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"For OAuth-authorized requests, the daily headers (","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Daily","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Daily-Remaining","depth":27,"on_screen":false,"role_description":"text"}]...
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More options for HubSpot rate limit handling with executeRequest
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HubSpot rate limit implementation strategy, rename chat
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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
against local-network Redis.
Per HubSpot call:
80–400 ms typical latency.
So the limiter adds something like
0.3% to each request's wall time
. The rate limit itself is forcing you to wait 4–40 ms between calls anyway; adding 0.5 ms of bookkeeping is invisible.
This is a pattern worth internalizing:
the rate limiter cannot be the bottleneck
, because the thing it's gating (HubSpot itself) is two to three orders of magnitude slower than the gating mechanism.
Things that would make it heavy (and how to avoid them)
Things that would make it heavy (and how to avoid them)
Multiple round trips per request
— if you
GET
then
INCR
then
ZADD
separately, you've tripled the latency and broken atomicity. Use Lua. One trip.
No connection pooling
— opening a TCP connection to Redis per request is 10× more expensive than the Redis op itself. Laravel's Redis facade pools by default; just don't fight it.
Persisting accounting to MySQL
— DB writes per API call would absolutely melt under 240/sec. Redis only. If you want analytics, sample or aggregate after the fact.
Synchronous polling waits
— if a worker is denied and busy-loops calling
acquire
every 10ms, you're putting 100/sec of pressure on Redis per worker for nothing. Either sleep for the
retry_ms
value the script returned, or push the job back to the queue with a delay and let the worker pick up other work.
Per-worker counters that you reconcile
— don't. Single shared Redis counter, period.
The real optimization isn't the limiter
The real optimization isn't the limiter
If you're actually facing 100k requests, the lever isn't shaving microseconds off the limiter — it's
collapsing 100 single-record calls into 1 batch call
. HubSpot's batch endpoints accept up to 100 records per call for read/create/update on most CRM objects:
100,000 single PATCHes → 1,000 batch updates.
100× reduction.
Each search call already returns up to 200 records via
limit: 200
.
Engagement updates (calls/meetings/notes) likewise have batch endpoints.
That single change takes you from 7 minutes against rate limits to under a minute, and drops your daily quota consumption by two orders of magnitude. Redis bookkeeping cost was never the problem; HubSpot's per-call cost was.
So to directly answer the worry: yes, two bucket touches per request, but it's one Redis round trip, sub-millisecond, and your infrastructure load profile for 100k requests is basically "Redis takes a nap while HubSpot rate-limits you."
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You said: Can I see rate limits per tenant (portal) in hubspot
You said: Can I see rate limits per tenant (portal) in hubspot
Can I see rate limits per tenant (portal) in hubspot
13:57
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Claude responded: Yes, and there are four different sources depending on what kind of view you want.
Claude responded: Yes, and there are four different sources depending on what kind of view you want.
Synthesized multiple HubSpot visibility methods for per-portal rate tracking
Synthesized multiple HubSpot visibility methods for per-portal rate tracking
Yes, and there are four different sources depending on what kind of view you want.
1. Response headers (per-call, real-time, per-portal automatically)
1. Response headers (per-call, real-time, per-portal automatically)
Every non-search response includes the burst-window headers, and they're already per-portal because the OAuth token in the request scopes them to that portal.
X-HubSpot-RateLimit-Max
,
X-HubSpot-RateLimit-Remaining
, and
X-HubSpot-RateLimit-Interval-Milliseconds
come back on every request
. Just read them off and store keyed by
portalId
. This is the cheapest, most accurate signal you can get — straight from HubSpot, no extra calls.
hubspot
hubspot
Two caveats:
For OAuth-authorized requests, the daily headers (
X-HubSpot-RateLimit-Daily
and
-Daily-Remaining...
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PostmanEditVIewWindowmelpHubSpot rate limit imple PostmanEditVIewWindowmelpHubSpot rate limit implementation strategyX-HubSpot-RateLimit-Secondly-RemainingDeprecated, ignoreSO Max=190, Interval=10000 means this portal is on a Pro/ Enterprise privately-distributedaoo aliyuius. I vousee max=110 .rs a oubnc VAutn nstal. max=250 means they nave uneAPI Limit Increase add-on.What vou cannot get via API• Search 5/sec limit - not exposed anywhere. Not in headers (search responses stripthem), not in any endpoint. It's a documented constant; vou assume it.• Daily usage broken out by app within a portal — the daily endpoint aggregates all privateapps. You can't tell from the API which app spent the budget.• Per-app burst limit programmatically — only inferred from Max in headers from a callthat app madePostman recide to fullv profile a vortallIhree calls in order.1. GET /account-info/v3/details →grab portalld. timeZone also notethe x-HunSnot-Rate imit-x resnonse neaders (this is vour burst nronle)2. GET /account-info/v3/api-usage/daily/private-apps → daily limit, current spend.reset time3. (Optional) Trigger a 429 deliberately on a sandbox to confirm policyName shape, but thisisn't necessary just for inspection.That gives vou evervthing HubSpot will tell vou about a specific portal's limits. Save the tworequests as a Postman collection with (faccess token}} and ((portal idi} ascollectionvariables and vou can profile anv portal in two clicks.Keep going in Claude CodeSwitch to Claude Code and let Claude work directiv in vour reno..running and testing as it goesWrite a message…Opus 4.7 Adaptive v40hellXx 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.HTTP https:pi.hubapi.com/account-info/v3/api-usaqe/daily/private-appsnttps://api.nubapi.com/account-info/vs/apl-usageo wy/private-apps= DocsParamsAuthorization • Headers 9 Body Scripts SettinasAuth TypeTokenThe authorization header will be automaticallygenerated when you send the reauest. Learnmore about Bearer Token authorizationGET Get EngGET httos://:{tokenl}"supoont Dally • In 47mNo environmentvg Save100% L24* AIVariables in request> All variablesThu 7 May 14:13:52v COLLECtIONscontacts› CRM ObjectseRM owners> CRM PipelinesDealsEngagements• D OLD ENGAGEMENTSGET list meetingsPST search moditied companiePOST search tasksGET read call> Post search callsGET list callsPOST meetings scheduledGET aet meetinaPOST get link to task> POST Create Contact with Associatio> HubsnotJournal & webhoooks v4POSt Get tokenGET get subscriptionsPOST create subscrintionGET Journal earliestGET lournal latesGeT https:ubspot-webhooks-lournal-nal.s.GET neyt offcotpost get loken prodDEL DELSTE CURSCRIPTION DEP PORTAI.GET DEAL WITH HISTORY pRODEpTICC VA©Authi> Properties> RESSARCHwCCADAUPOSt search contact bv ohonePOST search contact by emailPOST search meetinasPOST search notes> Post Search calls v3.IPOST Search related meetinas v3POST search dealsCAMIDONMCNTC> SPFCSELOWSConnect Git E Console 2 TermirCNeR-JHaMxlZoiNd.CookiesBodyJSONvPreviewe. Visualize"results":"name": "private-aops-aoi-calls-daiilv".."ucadel imi+". 1000000"collectedat": "2026-05-07T11:13:28.621Z""2026-05-08T04:00:002200 OK • 229 ms • 1.21 KB •(| .•CQ108Globals Vault Tools?000...
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-2764063263703231398
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PostmanEditVIewWindowmelpHubSpot rate limit imple PostmanEditVIewWindowmelpHubSpot rate limit implementation strategyX-HubSpot-RateLimit-Secondly-RemainingDeprecated, ignoreSO Max=190, Interval=10000 means this portal is on a Pro/ Enterprise privately-distributedaoo aliyuius. I vousee max=110 .rs a oubnc VAutn nstal. max=250 means they nave uneAPI Limit Increase add-on.What vou cannot get via API• Search 5/sec limit - not exposed anywhere. Not in headers (search responses stripthem), not in any endpoint. It's a documented constant; vou assume it.• Daily usage broken out by app within a portal — the daily endpoint aggregates all privateapps. You can't tell from the API which app spent the budget.• Per-app burst limit programmatically — only inferred from Max in headers from a callthat app madePostman recide to fullv profile a vortallIhree calls in order.1. GET /account-info/v3/details →grab portalld. timeZone also notethe x-HunSnot-Rate imit-x resnonse neaders (this is vour burst nronle)2. GET /account-info/v3/api-usage/daily/private-apps → daily limit, current spend.reset time3. (Optional) Trigger a 429 deliberately on a sandbox to confirm policyName shape, but thisisn't necessary just for inspection.That gives vou evervthing HubSpot will tell vou about a specific portal's limits. Save the tworequests as a Postman collection with (faccess token}} and ((portal idi} ascollectionvariables and vou can profile anv portal in two clicks.Keep going in Claude CodeSwitch to Claude Code and let Claude work directiv in vour reno..running and testing as it goesWrite a message…Opus 4.7 Adaptive v40hellXx 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.HTTP https:pi.hubapi.com/account-info/v3/api-usaqe/daily/private-appsnttps://api.nubapi.com/account-info/vs/apl-usageo wy/private-apps= DocsParamsAuthorization • Headers 9 Body Scripts SettinasAuth TypeTokenThe authorization header will be automaticallygenerated when you send the reauest. Learnmore about Bearer Token authorizationGET Get EngGET httos://:{tokenl}"supoont Dally • In 47mNo environmentvg Save100% L24* AIVariables in request> All variablesThu 7 May 14:13:52v COLLECtIONscontacts› CRM ObjectseRM owners> CRM PipelinesDealsEngagements• D OLD ENGAGEMENTSGET list meetingsPST search moditied companiePOST search tasksGET read call> Post search callsGET list callsPOST meetings scheduledGET aet meetinaPOST get link to task> POST Create Contact with Associatio> HubsnotJournal & webhoooks v4POSt Get tokenGET get subscriptionsPOST create subscrintionGET Journal earliestGET lournal latesGeT https:ubspot-webhooks-lournal-nal.s.GET neyt offcotpost get loken prodDEL DELSTE CURSCRIPTION DEP PORTAI.GET DEAL WITH HISTORY pRODEpTICC VA©Authi> Properties> RESSARCHwCCADAUPOSt search contact bv ohonePOST search contact by emailPOST search meetinasPOST search notes> Post Search calls v3.IPOST Search related meetinas v3POST search dealsCAMIDONMCNTC> SPFCSELOWSConnect Git E Console 2 TermirCNeR-JHaMxlZoiNd.CookiesBodyJSONvPreviewe. Visualize"results":"name": "private-aops-aoi-calls-daiilv".."ucadel imi+". 1000000"collectedat": "2026-05-07T11:13:28.621Z""2026-05-08T04:00:002200 OK • 229 ms • 1.21 KB •(| .•CQ108Globals Vault Tools?000...
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2026-05-07T11:13:56.835987+00:00
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/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778152436835_m2.jpg...
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PostmanEditVIewWindowmelpHubSpot rate limit imple PostmanEditVIewWindowmelpHubSpot rate limit implementation strategyX-HubSpot-RateLimit-Secondly-RemainingDeprecated, ignoreSO Max=190, Interval=10000 means this portal is on a Pro/ Enterprise privately-distributedaoo aliyuius. I vousee max=110 .rs a oubnc VAutn nstal. max=250 means they nave uneAPI Limit Increase add-on.What vou cannot get via API• Search 5/sec limit - not exposed anywhere. Not in headers (search responses stripthem), not in any endpoint. It's a documented constant; vou assume it• Daily usage broken out by app within a portal — the daily endpoint aggregates all privateapps. You can't tell from the API which app spent the budget.• Per-app burst limit programmatically — only inferred from Max in headers from a callthat app madePostman recide to fullv profile a vortallIhree calls in order.1. GET /account-info/v3/details →grab portalid. timeZone also notethe x-HunSnot-Rate imit-x resnonse neaders (this is vour burst nronle)2. GET /account-info/v3/api-usage/daily/private-apps → daily limit, current spend.reset time3. (Optional) Trigger a 429 deliberately on a sandbox to confirm policyName shape, but thisisn't necessary just for inspection.That gives vou evervthing HubSpot will tell vou about a specific portal's limits. Save the tworequests as a Postman collection with (faccess token}} and ((portal idi} ascollectionvariables and vou can profile anv portal in two clicks.Keep going in Claude CodeSwitch to Claude Code and let Claude work directiv in vour reno..running and testing as it goesWrite a message…Opus 4.7 Adaptive v"supoont Dally • In 47m100% L2Inu / May 14.13.00UparadeQ 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.https://api.hubapi.com/account-info/v3/api-usage/secondly/private-appshttps://api.hubapi.com/account-info/v3/api-usage/secondly/private-apps= DocsParamsAuthorization • Headers 9 Body Scripts SettinasAuth TypeTokenThe authorization header will be automaticallygenerated when you send the reauest. Learnmore about Bearer Token authorization{tokenl}hellGET httos://:No environmentv~ Saved ~ Cookiesv COLLECtIONscontacts› CRM ObjectseRM owners> CRM PipelinesDealsEngagements• D OLD ENGAGEMENTSGET list meetingsPST search moditied companiePOST search tasksGET read call> Post search callsGET list callsPOST meetings scheduledGET aet meetinaPOST get link to task> POST Create Contact with Associatio> HubsnotJournal & webhoooks v4POSt Get tokenGET get subscriptionsPOST create subscrintionGET Journal earliestGET lournal latesGeT https:ubspot-webhooks-lournal-nal.s.GET neyt offsetpost get loken prodDEL DELSTE CURSCRIPTION DEP PORTAI.GET DEAL WITH HISTORY pRODEpTICC VA©Authi> Properties> RESSARCHwCCADAUPOSt search contact bv ohonePOST search contact by emailPOST search meetinasPOST search notes> Post Search calls v3.IPOST Search related meetinas v3POST search dealsCAMIDONMCNTC> SPFCSELOWSConnect Git E Console 2 TermirVariables in requestG token> All variablesCNeR-JHaMxlZoiNd.BodyJSONvPreviewe. Visualize"results":"name": "private-aops-aoi-calls-dailv"."ucadel imi+". 1000000"collectedat": "2026-05-07T11:13:28.621Z""2026-05-08T04:00:002200 OK • 229 ms • 1.21 KB •(| .•=Q 8Globals Vault Tools?000...
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PostmanEditVIewWindowmelpHubSpot rate limit imple PostmanEditVIewWindowmelpHubSpot rate limit implementation strategyX-HubSpot-RateLimit-Secondly-RemainingDeprecated, ignoreSO Max=190, Interval=10000 means this portal is on a Pro/ Enterprise privately-distributedaoo aliyuius. I vousee max=110 .rs a oubnc VAutn nstal. max=250 means they nave uneAPI Limit Increase add-on.What vou cannot get via API• Search 5/sec limit - not exposed anywhere. Not in headers (search responses stripthem), not in any endpoint. It's a documented constant; vou assume it• Daily usage broken out by app within a portal — the daily endpoint aggregates all privateapps. You can't tell from the API which app spent the budget.• Per-app burst limit programmatically — only inferred from Max in headers from a callthat app madePostman recide to fullv profile a vortallIhree calls in order.1. GET /account-info/v3/details →grab portalid. timeZone also notethe x-HunSnot-Rate imit-x resnonse neaders (this is vour burst nronle)2. GET /account-info/v3/api-usage/daily/private-apps → daily limit, current spend.reset time3. (Optional) Trigger a 429 deliberately on a sandbox to confirm policyName shape, but thisisn't necessary just for inspection.That gives vou evervthing HubSpot will tell vou about a specific portal's limits. Save the tworequests as a Postman collection with (faccess token}} and ((portal idi} ascollectionvariables and vou can profile anv portal in two clicks.Keep going in Claude CodeSwitch to Claude Code and let Claude work directiv in vour reno..running and testing as it goesWrite a message…Opus 4.7 Adaptive v"supoont Dally • In 47m100% L2Inu / May 14.13.00UparadeQ 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.https://api.hubapi.com/account-info/v3/api-usage/secondly/private-appshttps://api.hubapi.com/account-info/v3/api-usage/secondly/private-apps= DocsParamsAuthorization • Headers 9 Body Scripts SettinasAuth TypeTokenThe authorization header will be automaticallygenerated when you send the reauest. Learnmore about Bearer Token authorization{tokenl}hellGET httos://:No environmentv~ Saved ~ Cookiesv COLLECtIONscontacts› CRM ObjectseRM owners> CRM PipelinesDealsEngagements• D OLD ENGAGEMENTSGET list meetingsPST search moditied companiePOST search tasksGET read call> Post search callsGET list callsPOST meetings scheduledGET aet meetinaPOST get link to task> POST Create Contact with Associatio> HubsnotJournal & webhoooks v4POSt Get tokenGET get subscriptionsPOST create subscrintionGET Journal earliestGET lournal latesGeT https:ubspot-webhooks-lournal-nal.s.GET neyt offsetpost get loken prodDEL DELSTE CURSCRIPTION DEP PORTAI.GET DEAL WITH HISTORY pRODEpTICC VA©Authi> Properties> RESSARCHwCCADAUPOSt search contact bv ohonePOST search contact by emailPOST search meetinasPOST search notes> Post Search calls v3.IPOST Search related meetinas v3POST search dealsCAMIDONMCNTC> SPFCSELOWSConnect Git E Console 2 TermirVariables in requestG token> All variablesCNeR-JHaMxlZoiNd.BodyJSONvPreviewe. Visualize"results":"name": "private-aops-aoi-calls-dailv"."ucadel imi+". 1000000"collectedat": "2026-05-07T11:13:28.621Z""2026-05-08T04:00:002200 OK • 229 ms • 1.21 KB •(| .•=Q 8Globals Vault Tools?000...
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2026-05-07T11:13:59.076399+00:00
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/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778152439076_m2.jpg...
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PostmanEditVIewWindowmelpHubSpot rate limit imple PostmanEditVIewWindowmelpHubSpot rate limit implementation strategyX-HubSpot-RateLimit-Secondly-RemainingDeprecated, ignoreSO Max=190, Interval=10000 means this portal is on a Pro/ Enterprise privately-distributedaoo aliyuius. I vousee max=110 .rs a oubnc VAutn nstal. max=250 means they nave uneAPI Limit Increase add-on.What vou cannot get via API• Search 5/sec limit - not exposed anywhere. Not in headers (search responses stripthem), not in any endpoint. It's a documented constant: vou assume it• Daily usage broken out by app within a portal - the daily endpoint aggregates all privateapps. You can't tell from the API which app spent the budget.• Per-app burst limit programmatically — only interred from Max in headers from a callthat app madePostman recide to fullv profile a vortallIhree calls in order.1. GET /account-info/v3/details →grab portalid. timeZone also notethe x-HunSnot-Rate imit-x resnonse neaders (this is vour burst nronle)2. GET /account-info/v3/api-usage/daily/private-apps → daily limit, current spend.reset time3. (Optional) Trigger a 429 deliberately on a sandbox to confirm policyName shape, but thisisn't necessary just for inspection.That gives vou evervthing HubSpot will tell vou about a specific portal's limits. Save the tworequests as a Postman collection with (faccess token}} and ((portal idi} ascollectionvariables and vou can profile anv portal in two clicks.Keep going in Claude CodeSwitch to Claude Code and let Claude work directiv in vour reno..running and testing as it goesWrite a message…Opus 4.7 Adaptive vhellQ 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 ciGET Get EnaGET httos://:HTTP https:pi.hubapi.com/account-info/v3/api-usaqe/secondlv/private-appshttps://api.hubapi.com/account-info/v3/api-usage/sec&hdly/private-apps= DocsParamsAuthorization • Headers 9 Body Scripts SettinasAuth TypeToken{tokenl}The authorization header will be automaticallygenerated when you send the reauest. Learnmore about Bearer Token authorization"supoont Dally • In 47mNo environmentv~ SaveCookies100% L2Inu / May 14.13.09UparadeCOLLECTIONScontacts› CRM ObjectseRM owners> CRM PipelinesDealsEngagements• D OLD ENGAGEMENTSGET list meetingsPST search moditied companiePOST search tasksGET read call> Post search callsGET list callsPOST meetings scheduledGET aet meetinaPOST get link to task> POST Create Contact with Associatio> HubsnotJournal & webhoooks v4POSt Get tokenGET get subscriptionsPOST create subscrintionGET Journal earliestGET lournal latesGeT https:ubspot-webhooks-lournal-nal.s.GET neyt offsetpost get loken prodDEL DELSTE CURSCRIPTION DEP PORTAI.GET DEAL WITH HISTORY pRODEpTICC VA©Authi> Properties> RESSARCHwCCADAUPOST search contact bv ohonePOSt search contact by emailPOST search meetinasPOST search notes> Post Search calls v3.IPOST Search related meetinas v3POST search dealsCAMIDONMCNTC> SPFCSELOWS$ Connect Git @ Console 2 TerminVariables in requestG tokenAll variablesCNeR-JHaMxlZoiNd.BodyCookies 17> HTMLvPreviewY^ Debua with Al l v<meta htto-equiv="Content-Tvoe" content="text/html.charset=utf-8" /><titlesFrror 404 Not Foundk/titlesALASUTTO CODAD ЛЛЙИILО-404 Not Found • 146 ms • 1.01 KB • @| ..Globals Vault Tools? 0 0 0...
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PostmanEditVIewWindowmelpHubSpot rate limit imple PostmanEditVIewWindowmelpHubSpot rate limit implementation strategyX-HubSpot-RateLimit-Secondly-RemainingDeprecated, ignoreSO Max=190, Interval=10000 means this portal is on a Pro/ Enterprise privately-distributedaoo aliyuius. I vousee max=110 .rs a oubnc VAutn nstal. max=250 means they nave uneAPI Limit Increase add-on.What vou cannot get via API• Search 5/sec limit - not exposed anywhere. Not in headers (search responses stripthem), not in any endpoint. It's a documented constant: vou assume it• Daily usage broken out by app within a portal - the daily endpoint aggregates all privateapps. You can't tell from the API which app spent the budget.• Per-app burst limit programmatically — only interred from Max in headers from a callthat app madePostman recide to fullv profile a vortallIhree calls in order.1. GET /account-info/v3/details →grab portalid. timeZone also notethe x-HunSnot-Rate imit-x resnonse neaders (this is vour burst nronle)2. GET /account-info/v3/api-usage/daily/private-apps → daily limit, current spend.reset time3. (Optional) Trigger a 429 deliberately on a sandbox to confirm policyName shape, but thisisn't necessary just for inspection.That gives vou evervthing HubSpot will tell vou about a specific portal's limits. Save the tworequests as a Postman collection with (faccess token}} and ((portal idi} ascollectionvariables and vou can profile anv portal in two clicks.Keep going in Claude CodeSwitch to Claude Code and let Claude work directiv in vour reno..running and testing as it goesWrite a message…Opus 4.7 Adaptive vhellQ 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 ciGET Get EnaGET httos://:HTTP https:pi.hubapi.com/account-info/v3/api-usaqe/secondlv/private-appshttps://api.hubapi.com/account-info/v3/api-usage/sec&hdly/private-apps= DocsParamsAuthorization • Headers 9 Body Scripts SettinasAuth TypeToken{tokenl}The authorization header will be automaticallygenerated when you send the reauest. Learnmore about Bearer Token authorization"supoont Dally • In 47mNo environmentv~ SaveCookies100% L2Inu / May 14.13.09UparadeCOLLECTIONScontacts› CRM ObjectseRM owners> CRM PipelinesDealsEngagements• D OLD ENGAGEMENTSGET list meetingsPST search moditied companiePOST search tasksGET read call> Post search callsGET list callsPOST meetings scheduledGET aet meetinaPOST get link to task> POST Create Contact with Associatio> HubsnotJournal & webhoooks v4POSt Get tokenGET get subscriptionsPOST create subscrintionGET Journal earliestGET lournal latesGeT https:ubspot-webhooks-lournal-nal.s.GET neyt offsetpost get loken prodDEL DELSTE CURSCRIPTION DEP PORTAI.GET DEAL WITH HISTORY pRODEpTICC VA©Authi> Properties> RESSARCHwCCADAUPOST search contact bv ohonePOSt search contact by emailPOST search meetinasPOST search notes> Post Search calls v3.IPOST Search related meetinas v3POST search dealsCAMIDONMCNTC> SPFCSELOWS$ Connect Git @ Console 2 TerminVariables in requestG tokenAll variablesCNeR-JHaMxlZoiNd.BodyCookies 17> HTMLvPreviewY^ Debua with Al l v<meta htto-equiv="Content-Tvoe" content="text/html.charset=utf-8" /><titlesFrror 404 Not Foundk/titlesALASUTTO CODAD ЛЛЙИILО-404 Not Found • 146 ms • 1.01 KB • @| ..Globals Vault Tools? 0 0 0...
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/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778152444277_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....
|
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chat","depth":21,"bounds":{"left":0.48537233,"top":0.026336791,"width":0.010638298,"height":0.025538707},"on_screen":true,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Claude finished the response","depth":21,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"You said: So just a solution for rate limit implementation.","depth":20,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"You said: So just a solution for rate limit implementation.","depth":21,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"updating activity (engagement, meeting, call, note)","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"searching for a company or contact","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"updating deal","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"patching IDs\nReview what the limit is at the moment in the HubSpot documentation and then think of the solution:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"where to store it","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"how to read it","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"how to work with multiple jobs","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines","depth":25,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"). Example of such a error Client error:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST https://api.hubapi.com/crm/v3/objects/contact/search","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"resulted in a","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"429 Too Many Requests","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"response: {\"status\":\"error\",\"message\":\"You have reached your secondly limit.\",\"errorType\":\"RATE_LIMIT\",\"correlationId\":\"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Show more","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"10:42","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"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. Don't busy-loop.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"After the call, parse","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Max","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Interval-Milliseconds","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and store them in","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:meta:{portalId}","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"For search specifically, since headers don't come back, the local counter","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"is","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"not","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"on 429 (you really did make that request).","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to work with multiple jobs","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to work with multiple jobs","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Concurrency cap","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"via","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Queue::throttle()","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Per-tenant fairness","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Priority lanes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot-priority","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot-bulk","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Backoff on 429","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— respect the","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry-After","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"header. 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Each page is its own API call and competes for tokens with everything else.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Two practical rules:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Don't hold the worker idle while paginating.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"processing","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"of page N as a separate job, and queue a \"fetch page N+1\" job with a delay equal to the wait time. Each page becomes its own atomic unit.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Always batch where HubSpot offers it.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/{type}/batch/{read|update|create}","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"accepts up to 100 IDs per call. For your \"patching IDs\" flow this is the difference between 600 calls and 6.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Walkthrough: 600 opportunities","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Walkthrough: 600 opportunities","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Sane flow:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Identify deltas","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— if you can avoid Search at all, do. Use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Batch read","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"current state —","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/deals/batch/read","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with 100 IDs per call → 6 calls. Burst weight 6.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Compute diff","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"in your code (no API).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Batch update","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"—","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/deals/batch/update","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with 100 per call → 6 calls.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Associations","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"—","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v4/associations/{from}/{to}/batch/create","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"if needed → another few batch calls.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Recognition: pre-check denies if","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"<weight>","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"slots aren't free; on 429 the response surfaces","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"policyName","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(DAILY/SECONDLY) — log it, push the job back with","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry-After","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"delay, increment a metric so you can alarm on sustained 429 rate.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Pitfalls & bottlenecks worth pre-empting","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Pitfalls & bottlenecks worth pre-empting","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Daily quota is shared across the whole portal","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search is uniquely fragile","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":": separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hs_object_id","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"ranges or by","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"windows.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry storms after HubSpot incidents","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Webhook → sync → webhook loops","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"jy_last_sync_id","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":") and ignore webhooks where it just changed.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Token refresh","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"expires_in - 60s","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", single-flight the refresh (only one worker refreshes; others wait on a lock).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search latency for fresh writes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Clock skew","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— Redis is authoritative. Don't use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"time()","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"from each worker for window scoring; use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"redis.call('TIME')","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"inside the Lua script.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Dead workers with held tokens","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The 5% error rule","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Phantom contention with Laravel","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"RateLimited","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"middleware","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":": it's per-job-class by default, not per-portal. If your","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"SyncDealJob","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"UpdateActivityJob","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"HubspotRateLimiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"service that everything routes through.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.","depth":25,"on_screen":false,"role_description":"text"}]...
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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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HubSpot rate limit implementation strategy, rename chat
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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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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
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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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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
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
[...
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Either separate queues per portal or a fair-share scheduler.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Priority lanes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— webhook-driven updates (user-visible latency) should outrank background batch syncs. 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.
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
[...
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ClaudeFileEditVIewWindowmelt= HubSpot rate limit i ClaudeFileEditVIewWindowmelt= HubSpot rate limit implementation strategyX-HubSpot-RateLimit-Secondly-RemainingDeprecated, ignoreSO Max=190, Interval=10000 means this portal is on a Pro/ Enterprise privately-distributedaoo aliyuius. I vousee max=110 .rs a oudnc VAutn instal. max=250 means they nave theAPI Limit Increase add-on.What vou cannot get via API• Search 5/sec limit - not exposed anywhere. Not in headers (search responses stripthem), not in any endpoint. It's a documented constant: vou assume it• Daily usage broken out by app within a portal - the daily endpoint aggregates all privateapps. You can't tell from the API which app spent the budget.• Per-app burst limit programmatically — only interred from Max in headers from a callthat app madePostman recide to fullv profile a vortallIhree calls in order.1. GET /account-info/v3/details →grab portalid. timeZone also notethe x-HunSnot-Rate imit-x resnonse neaders (this is vour burst nronle)2. GET /account-info/v3/api-usage/daily/private-apps → daily limit, current spend.reset time3. (Optional) Trigger a 429 deliberately on a sandbox to confirm policyName shape, but thisisn't necessary just for inspection.That gives vou evervthing HubSpot will tell vou about a specific portal's limits. Save the tworequests as a Postman collection with (faccess token}} and ((portal idi} ascollectionvariables and vou can profile anv portal in two clicks.supoont Dally • In 40 m100% L2Inu / May 14.14.10UparadeQ Searchn. You retain editing access and other members are read-only. View team permissions to see who can edit, or upgrade to restore collaboration.HTTP https:pi.hubapi.com/account-info/v3/api-usaqe/secondlv/private-appsnttps://api.nubapi.com/account-info/vs/api-usc= DocsParamsAuthorization • Headers 9 Body Scripts SettinasAuth TypeRearer TokenTokenine autnorization neader will oe automaticallygenerated when you send the reauest. Learnmore about Bearer Token authorizationGET read ciGET Get Ena{tokenl}GET httos://:No environmentv~ SaveCookiesVariables in requestG tokenAll variablesCNeR-JHaMxlZoiNO.Body Cookies 1 Headers 16 Test ResultsT, HTMLvPreviewY^ Debua with Al l v404 Not Found 146 ms • 1.01 KB • (a .•<meta htto-eauiv="Content-Tvoe" content="text/html:charset=utf-g" /><titlesFrror 404 Not Foundk/titlesALASUTTO CODOD ЛЛЛИЛLОSKeep going in Claude CodeSwitch to Claude Code and let Claude work directiv in vour reno..running and testing as it goesI can see dailvOpus 4.7 AdaptiveGlobals Vault Tools ? 0 0 0...
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ClaudeFileEditVIewWindowmelt= HubSpot rate limit i ClaudeFileEditVIewWindowmelt= HubSpot rate limit implementation strategyX-HubSpot-RateLimit-Secondly-RemainingDeprecated, ignoreSO Max=190, Interval=10000 means this portal is on a Pro/ Enterprise privately-distributedaoo aliyuius. I vousee max=110 .rs a oudnc VAutn instal. max=250 means they nave theAPI Limit Increase add-on.What vou cannot get via API• Search 5/sec limit - not exposed anywhere. Not in headers (search responses stripthem), not in any endpoint. It's a documented constant: vou assume it• Daily usage broken out by app within a portal - the daily endpoint aggregates all privateapps. You can't tell from the API which app spent the budget.• Per-app burst limit programmatically — only interred from Max in headers from a callthat app madePostman recide to fullv profile a vortallIhree calls in order.1. GET /account-info/v3/details →grab portalid. timeZone also notethe x-HunSnot-Rate imit-x resnonse neaders (this is vour burst nronle)2. GET /account-info/v3/api-usage/daily/private-apps → daily limit, current spend.reset time3. (Optional) Trigger a 429 deliberately on a sandbox to confirm policyName shape, but thisisn't necessary just for inspection.That gives vou evervthing HubSpot will tell vou about a specific portal's limits. Save the tworequests as a Postman collection with (faccess token}} and ((portal idi} ascollectionvariables and vou can profile anv portal in two clicks.supoont Dally • In 40 m100% L2Inu / May 14.14.10UparadeQ Searchn. You retain editing access and other members are read-only. View team permissions to see who can edit, or upgrade to restore collaboration.HTTP https:pi.hubapi.com/account-info/v3/api-usaqe/secondlv/private-appsnttps://api.nubapi.com/account-info/vs/api-usc= DocsParamsAuthorization • Headers 9 Body Scripts SettinasAuth TypeRearer TokenTokenine autnorization neader will oe automaticallygenerated when you send the reauest. Learnmore about Bearer Token authorizationGET read ciGET Get Ena{tokenl}GET httos://:No environmentv~ SaveCookiesVariables in requestG tokenAll variablesCNeR-JHaMxlZoiNO.Body Cookies 1 Headers 16 Test ResultsT, HTMLvPreviewY^ Debua with Al l v404 Not Found 146 ms • 1.01 KB • (a .•<meta htto-eauiv="Content-Tvoe" content="text/html:charset=utf-g" /><titlesFrror 404 Not Foundk/titlesALASUTTO CODOD ЛЛЛИЛLОSKeep going in Claude CodeSwitch to Claude Code and let Claude work directiv in vour reno..running and testing as it goesI can see dailvOpus 4.7 AdaptiveGlobals Vault Tools ? 0 0 0...
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PostmanEditVIewWindowmelpHubSpot rate limit imple PostmanEditVIewWindowmelpHubSpot rate limit implementation strategyX-HubSpot-RateLimit-Secondly-RemainingDeprecated, ignoreSO Max=190, Interval=10000 means this portal is on a Pro/ Enterprise privately-distributedaoo aliyuius. I vousee max=110 .rs a oubnc VAutn nstal. max=250 means they nave uneAPI Limit Increase add-on.What vou cannot get via API• Search 5/sec limit - not exposed anywhere. Not in headers (search responses stripthem), not in any endpoint. It's a documented constant; vou assume it.• Daily usage broken out by app within a portal - the daily endpoint aggregates all privateapps. You can't tell from the API which app spent the budget.• Per-app burst limit programmatically — only interred from Max in headers from a callthat app madePostman recide to fullv profile a vortallIhree calls in order.1. GET /account-info/v3/details →grab portalld. timeZone also notethe x-HunSnot-Rate imit-x resnonse neaders (this is vour burst nronle)2. GET /account-info/v3/api-usage/daily/private-apps → daily limit, current spend.reset time3. (Optional) Trigger a 429 deliberately on a sandbox to confirm policyName shape, but thisisn't necessary just for inspection.That gives vou evervthing HubSpot will tell vou about a specific portal's limits. Save the tworequests as a Postman collection with (faccess token}} and ((portal idi} ascollectionvariables and vou can profile anv portal in two clicks.Keep going in Claude CodeSwitch to Claude Code and let Claude work directiv in vour reno.running and testing as it goesI can see dailvOpus 4.7 Adaptive vInu / May 14.14.12UparadeQ 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.https://lapi.hubapi.com/account-info/v3/api-usaqe/daily/private-appsnttps://api.nubapi.com/account-info/vs/api-usage= DocsParamsAuthorization • Headers 9 Body ScriptsSettinasAuth TypeTokenThe authorization header will be automaticallygenerated when you send the reauest. Learnmore about Bearer Token authorizationGET read ci{tokenl}GET httos://:supoont Dally • In 40 mNo environmentva Save2 ~ Cookies100% L24* AIVariables in requestG tokenAll variablesCOLLECTIONScontacts› CRM ObjectseRM owners> CRM Pipelines> DealsEngagements• D OLD ENGAGEMENTSGET list meetingsPST search moditied companiePOST search tasksGET read call> Post search callsGET list callsPOST meetings scheduledGET aet meetinaPOST get link to task> POST Create Contact with Associatio> HubsnotJournal & webhoooks v4POSt Get tokenGET get subscriptionsPOST create subscrintionGET Journal earliestGET lournal latesGeT https:ubspot-webhooks-lournal-nal.s.GET neyt offcotpost get loken prodDEL DELSTE CURSCRIPTION DEP PORTAL.GET DEAL WITH HISTORY pRODEpTICC VA©Authi> Properties> RESSARCHwCCADAUPOST search contact bv ohonePOSt search contact by emailPOST search meetinasPOST search notes> Post Search calls v3.IPOST Search related meetinas v3POST search dealsCAMIDONMCNTC> SPFCSELOWS$ Connect Git @ Console TermirCNeR-JHaMxlZoiNd.BOdVCookies 17> HTML vPreviewY^ Debua with Al l v<meta htto-equiv="Content-Tvoe" content="text/html.charset=utf-8" /><titlesFrror 404 Not Foundk/titlesALASUTTO CODAD ЛЛЙИILО-404 Not Found • 146 ms • 1.01 KB • @| ..Globals Vault Tools? 0 0 0...
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PostmanEditVIewWindowmelpHubSpot rate limit imple PostmanEditVIewWindowmelpHubSpot rate limit implementation strategyX-HubSpot-RateLimit-Secondly-RemainingDeprecated, ignoreSO Max=190, Interval=10000 means this portal is on a Pro/ Enterprise privately-distributedaoo aliyuius. I vousee max=110 .rs a oubnc VAutn nstal. max=250 means they nave uneAPI Limit Increase add-on.What vou cannot get via API• Search 5/sec limit - not exposed anywhere. Not in headers (search responses stripthem), not in any endpoint. It's a documented constant; vou assume it.• Daily usage broken out by app within a portal - the daily endpoint aggregates all privateapps. You can't tell from the API which app spent the budget.• Per-app burst limit programmatically — only interred from Max in headers from a callthat app madePostman recide to fullv profile a vortallIhree calls in order.1. GET /account-info/v3/details →grab portalld. timeZone also notethe x-HunSnot-Rate imit-x resnonse neaders (this is vour burst nronle)2. GET /account-info/v3/api-usage/daily/private-apps → daily limit, current spend.reset time3. (Optional) Trigger a 429 deliberately on a sandbox to confirm policyName shape, but thisisn't necessary just for inspection.That gives vou evervthing HubSpot will tell vou about a specific portal's limits. Save the tworequests as a Postman collection with (faccess token}} and ((portal idi} ascollectionvariables and vou can profile anv portal in two clicks.Keep going in Claude CodeSwitch to Claude Code and let Claude work directiv in vour reno.running and testing as it goesI can see dailvOpus 4.7 Adaptive vInu / May 14.14.12UparadeQ 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.https://lapi.hubapi.com/account-info/v3/api-usaqe/daily/private-appsnttps://api.nubapi.com/account-info/vs/api-usage= DocsParamsAuthorization • Headers 9 Body ScriptsSettinasAuth TypeTokenThe authorization header will be automaticallygenerated when you send the reauest. Learnmore about Bearer Token authorizationGET read ci{tokenl}GET httos://:supoont Dally • In 40 mNo environmentva Save2 ~ Cookies100% L24* AIVariables in requestG tokenAll variablesCOLLECTIONScontacts› CRM ObjectseRM owners> CRM Pipelines> DealsEngagements• D OLD ENGAGEMENTSGET list meetingsPST search moditied companiePOST search tasksGET read call> Post search callsGET list callsPOST meetings scheduledGET aet meetinaPOST get link to task> POST Create Contact with Associatio> HubsnotJournal & webhoooks v4POSt Get tokenGET get subscriptionsPOST create subscrintionGET Journal earliestGET lournal latesGeT https:ubspot-webhooks-lournal-nal.s.GET neyt offcotpost get loken prodDEL DELSTE CURSCRIPTION DEP PORTAL.GET DEAL WITH HISTORY pRODEpTICC VA©Authi> Properties> RESSARCHwCCADAUPOST search contact bv ohonePOSt search contact by emailPOST search meetinasPOST search notes> Post Search calls v3.IPOST Search related meetinas v3POST search dealsCAMIDONMCNTC> SPFCSELOWS$ Connect Git @ Console TermirCNeR-JHaMxlZoiNd.BOdVCookies 17> HTML vPreviewY^ Debua with Al l v<meta htto-equiv="Content-Tvoe" content="text/html.charset=utf-8" /><titlesFrror 404 Not Foundk/titlesALASUTTO CODAD ЛЛЙИILО-404 Not Found • 146 ms • 1.01 KB • @| ..Globals Vault Tools? 0 0 0...
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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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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}]...
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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...
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2390
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2026-05-07T11:14:19.774853+00:00
|
/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778152459774_m2.jpg...
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Claude
|
Claude
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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
Show more
10:42
Retry
Edit
Copy
Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
Copy
Give positive feedback
Give negative feedback
Retry
You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
Retry
Edit
Copy
Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
Copy to clipboard
php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.
Copy
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Retry
You said: Ok, so what will happen each time I make a request?
You said: Ok, so what will happen each time I make a request?
Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?
13:44
Retry
Edit
Copy
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Analyzed per-request overhead concerns and quantified Redis performance impact
Analyzed per-request overhead concerns and quantified Redis performance impact
Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out. But the overhead is much smaller than it sounds, and importantly, it's nowhere near your bottleneck.
What "two buckets per request" actually costs
What "two buckets per request" actually costs
Each request makes
one round trip to Redis
— not two — because you fold both bucket checks into a single Lua script that runs atomically server-side:
Copy to clipboard
lua
-- KEYS[1] = burst bucket key (or search)
-- KEYS[2] = daily bucket key
-- ARGV[1] = now (ms) ARGV[2] = window_ms
-- ARGV[3] = bucket_max ARGV[4] = daily_max
-- ARGV[5] = request_id ARGV[6] = daily_ttl
-- Trim sliding window
redis
.
call
(
'ZREMRANGEBYSCORE'
,
KEYS
[
1
]
,
0
,
ARGV
[
1
]
-
ARGV
[
2
]
)
local
burst_used
=
redis
.
call
(
'ZCARD'
,
KEYS
[
1
]
)
local
daily_used
=
tonumber
(
redis
.
call
(
'GET'
,
KEYS
[
2
]
)
or
'0'
)
if
burst_used
>=
tonumber
(
ARGV
[
3
]
)
then
-- Tell caller how long to sleep until oldest entry expires
local
oldest
=
redis
.
call
(
'ZRANGE'
,
KEYS
[
1
]
,
0
,
0
,
'WITHSCORES'
)
return
{
0
,
'BURST'
,
(
oldest
[
2
]
+
ARGV
[
2
]
)
-
ARGV
[
1
]
}
end
if
daily_used
>=
tonumber
(
ARGV
[
4
]
)
then
return
{
0
,
'DAILY'
,
-
1
}
end
redis
.
call
(
'ZADD'
,
KEYS
[
1
]
,
ARGV
[
1
]
,
ARGV
[
5
]
)
redis
.
call
(
'PEXPIRE'
,
KEYS
[
1
]
,
ARGV
[
2
]...
|
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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. 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Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"13:40","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Edit","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"Claude responded: They're independent buckets — with one shared bucket on top.","depth":21,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Claude responded: They're independent buckets — with one shared bucket on top.","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Analyzed independent rate limit buckets and their practical interactions","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Analyzed independent rate limit buckets and their practical interactions","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"They're independent buckets — with one shared bucket on top.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"The three buckets and what each call hits","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"The three buckets and what each call hits","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"┌──────────────────┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search request ──→ │ Search: 5/sec │ ──┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"└──────────────────┘ │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"├──→ ┌─────────────────┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"┌──────────────────┐ │ │ Daily: 250k–1M │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"└──────────────────┘ └─────────────────┘","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"What this means practically","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"What this means practically","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting search secondly limit (5/s):","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Only further","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/search","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting burst limit (190/10s):","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting daily limit:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"So can one type of request block the other?","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"So can one type of request block the other?","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search → Burst:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"No.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Burst → Search:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"No.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Saturating burst leaves Search's 5/s untouched.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Either → Daily:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Yes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", but only by contributing to the same daily counter. It's volume that kills you here, not the type.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"In theory you can sustain ~50 search + ~190 other =","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"~240 calls per 10 seconds","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"indefinitely (until daily runs out), because the two buckets bill independently.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Implementation consequence","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Implementation consequence","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"php","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'search'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// for search endpoints only","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'burst'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// for everything else","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'daily'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// every call, both kinds","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('search')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"+","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('daily')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". A batch update calls","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('burst')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"+","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('daily')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":".","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"One subtle pitfall: when you backoff on a 429,","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"inspect","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"policyName","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"in the response body","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to know which bucket actually fired. If you see","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"SECONDLY","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The other subtlety: the burst response gives you","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give positive feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give negative feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"You said: Ok, so what will happen each time I make a request?","depth":20,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"You said: Ok, so what will happen each time I make a request?","depth":21,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"13:44","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Edit","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.","depth":21,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Analyzed per-request overhead concerns and quantified Redis performance impact","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Analyzed per-request overhead concerns and quantified Redis performance impact","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out. But the overhead is much smaller than it sounds, and importantly, it's nowhere near your bottleneck.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"What \"two buckets per request\" actually costs","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"What \"two buckets per request\" actually costs","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Each request makes","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"one round trip to Redis","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— not two — because you fold both bucket checks into a single Lua script that runs atomically server-side:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to 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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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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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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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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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Retry
You said: Ok, so what will happen each time I make a request?
You said: Ok, so what will happen each time I make a request?
Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?
13:44
Retry
Edit
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Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Analyzed per-request overhead concerns and quantified Redis performance impact
Analyzed per-request overhead concerns and quantified Redis performance impact
Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out. But the overhead is much smaller than it sounds, and importantly, it's nowhere near your bottleneck.
What "two buckets per request" actually costs
What "two buckets per request" actually costs
Each request makes
one round trip to Redis
— not two — because you fold both bucket checks into a single Lua script that runs atomically server-side:
Copy to clipboard
lua
-- KEYS[1] = burst bucket key (or search)
-- KEYS[2] = daily bucket key
-- ARGV[1] = now (ms) ARGV[2] = window_ms
-- ARGV[3] = bucket_max ARGV[4] = daily_max
-- ARGV[5] = request_id ARGV[6] = daily_ttl
-- Trim sliding window
redis
.
call
(
'ZREMRANGEBYSCORE'
,
KEYS
[
1
]
,
0
,
ARGV
[
1
]
-
ARGV
[
2
]
)
local
burst_used
=
redis
.
call
(
'ZCARD'
,
KEYS
[
1
]
)
local
daily_used
=
tonumber
(
redis
.
call
(
'GET'
,
KEYS
[
2
]
)
or
'0'
)
if
burst_used
>=
tonumber
(
ARGV
[
3
]
)
then
-- Tell caller how long to sleep until oldest entry expires
local
oldest
=
redis
.
call
(
'ZRANGE'
,
KEYS
[
1
]
,
0
,
0
,
'WITHSCORES'
)
return
{
0
,
'BURST'
,
(
oldest
[
2
]
+
ARGV
[
2
]
)
-
ARGV
[
1
]
}
end
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
]...
|
NULL
|
NULL
|
NULL
|
NULL
|
|
2396
|
104
|
6
|
2026-05-07T11:14:21.770351+00:00
|
/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778152461770_m2.jpg...
|
Claude
|
Claude
|
True
|
NULL
|
monitor_2
|
NULL
|
NULL
|
NULL
|
NULL
|
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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
Show more
10:42
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Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
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
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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
....
|
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Pro","depth":15,"bounds":{"left":0.0043218085,"top":0.6943336,"width":0.037898935,"height":0.01915403},"on_screen":true,"role_description":"pop-up button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Get apps and extensions","depth":15,"bounds":{"left":0.08277926,"top":0.6943336,"width":0.007978723,"height":0.01915403},"on_screen":true,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"HubSpot rate limit implementation strategy, rename chat","depth":19,"bounds":{"left":0.043218084,"top":0.02793296,"width":0.09773936,"height":0.022346368},"on_screen":true,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"HubSpot rate limit implementation 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chat","depth":21,"bounds":{"left":0.48537233,"top":0.026336791,"width":0.010638298,"height":0.025538707},"on_screen":true,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Claude finished the response","depth":21,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"You said: So just a solution for rate limit implementation.","depth":20,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"You said: So just a solution for rate limit implementation.","depth":21,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"updating activity (engagement, meeting, call, note)","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"searching for a company or contact","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"updating deal","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"patching IDs\nReview what the limit is at the moment in the HubSpot documentation and then think of the solution:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"where to store it","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"how to read it","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"how to work with multiple jobs","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines","depth":25,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"). Example of such a error Client error:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST https://api.hubapi.com/crm/v3/objects/contact/search","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"resulted in a","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"429 Too Many Requests","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"response: {\"status\":\"error\",\"message\":\"You have reached your secondly limit.\",\"errorType\":\"RATE_LIMIT\",\"correlationId\":\"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Show more","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"10:42","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Edit","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"Claude responded: Current HubSpot limits (Apr 2026)","depth":21,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Claude responded: Current HubSpot limits (Apr 2026)","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Current HubSpot limits (Apr 2026)","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Current HubSpot limits (Apr 2026)","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". The burst window is per app; the daily quota is per portal.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". That's the limit the 429 in your example is hitting (","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"secondly limit","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"on","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/crm/v3/objects/contact/search","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"). It's a separate bucket from the 190/10s, and critically, search responses do not include the","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-*","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"headers","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— you can't read remaining capacity off the response, you have to track it yourself.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Non-search endpoints return","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Max","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", and","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Interval-Milliseconds","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". The daily headers are not returned for OAuth-authenticated calls","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— that's enforced for marketplace listing, but it's also a sane operational target.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"So you have","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"four buckets","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Where to store it","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Where to store it","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"INCR","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"EXPIRE","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"plus Lua scripting for true CAS semantics.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Key layout I'd use:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:search:{portalId} ZSET (sliding 1s)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:meta:{portalId} HASH (last seen headers)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The two short-window buckets are sliding-window-log (Redis ZSET, score =","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"microtime","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", member = unique request id). 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Get this once and cache it.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to read it","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to read it","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Two-phase: optimistic pre-check, authoritative post-update.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Before a call, run the Lua acquire script. If it returns \"denied,\" sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"After the call, parse","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Max","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Interval-Milliseconds","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and store them in","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:meta:{portalId}","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"For search specifically, since headers don't come back, the local counter","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"is","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"not","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"on 429 (you really did make that request).","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to work with multiple jobs","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to work with multiple jobs","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Concurrency cap","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"via","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Queue::throttle()","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Per-tenant fairness","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Priority lanes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot-priority","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot-bulk","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Backoff on 429","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— respect the","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry-After","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"header. 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Each page is its own API call and competes for tokens with everything else.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Two practical rules:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Don't hold the worker idle while paginating.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"processing","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"of page N as a separate job, and queue a \"fetch page N+1\" job with a delay equal to the wait time. Each page becomes its own atomic unit.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Always batch where HubSpot offers it.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/{type}/batch/{read|update|create}","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"accepts up to 100 IDs per call. For your \"patching IDs\" flow this is the difference between 600 calls and 6.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Walkthrough: 600 opportunities","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Walkthrough: 600 opportunities","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Sane flow:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Identify deltas","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— if you can avoid Search at all, do. Use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Batch read","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"current state —","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/deals/batch/read","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with 100 IDs per call → 6 calls. Burst weight 6.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Compute diff","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"in your code (no API).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Batch update","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"—","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/deals/batch/update","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with 100 per call → 6 calls.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Associations","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"—","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v4/associations/{from}/{to}/batch/create","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"if needed → another few batch calls.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Recognition: pre-check denies if","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"<weight>","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"slots aren't free; on 429 the response surfaces","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"policyName","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(DAILY/SECONDLY) — log it, push the job back with","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry-After","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"delay, increment a metric so you can alarm on sustained 429 rate.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Pitfalls & bottlenecks worth pre-empting","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Pitfalls & bottlenecks worth pre-empting","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Daily quota is shared across the whole portal","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search is uniquely fragile","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":": separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hs_object_id","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"ranges or by","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"windows.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry storms after HubSpot incidents","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Webhook → sync → webhook loops","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"jy_last_sync_id","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":") and ignore webhooks where it just changed.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Token refresh","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"expires_in - 60s","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", single-flight the refresh (only one worker refreshes; others wait on a lock).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search latency for fresh writes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Clock skew","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— Redis is authoritative. Don't use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"time()","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"from each worker for window scoring; use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"redis.call('TIME')","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"inside the Lua script.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Dead workers with held tokens","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The 5% error rule","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Phantom contention with Laravel","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"RateLimited","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"middleware","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":": it's per-job-class by default, not per-portal. If your","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"SyncDealJob","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"UpdateActivityJob","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"HubspotRateLimiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"service that everything routes through.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give positive feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give negative feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"You said: What will happen if there are two different types of rate limits?","depth":20,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"You said: What will happen if there are two different types of rate limits?","depth":21,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"What will happen if there are two different types of rate limits?","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search requests that have 5 requests per second","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"13:40","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Edit","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"Claude responded: They're independent buckets — with one shared bucket on top.","depth":21,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Claude responded: They're independent buckets — with one shared bucket on top.","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Analyzed independent rate limit buckets and their practical interactions","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Analyzed independent rate limit buckets and their practical interactions","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"They're independent buckets — with one shared bucket on top.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"The three buckets and what each call hits","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"The three buckets and what each call hits","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"┌──────────────────┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search request ──→ │ Search: 5/sec │ ──┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"└──────────────────┘ │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"├──→ ┌─────────────────┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"┌──────────────────┐ │ │ Daily: 250k–1M │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"└──────────────────┘ └─────────────────┘","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"What this means practically","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"What this means practically","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting search secondly limit (5/s):","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Only further","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/search","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting burst limit (190/10s):","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting daily limit:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"So can one type of request block the other?","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"So can one type of request block the other?","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search → Burst:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"No.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Burst → Search:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"No.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Saturating burst leaves Search's 5/s untouched.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Either → Daily:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Yes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", but only by contributing to the same daily counter. It's volume that kills you here, not the type.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"In theory you can sustain ~50 search + ~190 other =","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"~240 calls per 10 seconds","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"indefinitely (until daily runs out), because the two buckets bill independently.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Implementation consequence","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Implementation consequence","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"php","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'search'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// for search endpoints only","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'burst'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// for everything else","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'daily'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// every call, both kinds","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('search')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"+","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('daily')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". A batch update calls","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('burst')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"+","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('daily')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":".","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"One subtle pitfall: when you backoff on a 429,","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"inspect","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"policyName","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"in the response body","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to know which bucket actually fired. If you see","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"SECONDLY","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The other subtlety: the burst response gives you","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give positive feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give negative feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"You said: Ok, so what will happen each time I make a request?","depth":20,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"You said: Ok, so what will happen each time I make a request?","depth":21,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"13:44","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Edit","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.","depth":21,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Analyzed per-request overhead concerns and quantified Redis performance impact","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Analyzed per-request overhead concerns and quantified Redis performance impact","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out. But the overhead is much smaller than it sounds, and importantly, it's nowhere near your bottleneck.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"What \"two buckets per request\" actually costs","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"What \"two buckets per request\" actually costs","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Each request makes","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"one round trip to Redis","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— not two — because you fold both bucket checks into a single Lua script that runs atomically server-side:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"lua","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-- KEYS[1] = burst bucket key (or search)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-- KEYS[2] = daily bucket key","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-- ARGV[1] = now (ms) ARGV[2] = window_ms","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-- ARGV[3] = bucket_max ARGV[4] = daily_max","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-- ARGV[5] = request_id ARGV[6] = daily_ttl","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-- Trim sliding 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"}]...
|
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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?
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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
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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.
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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.
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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.
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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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Retry
You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
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Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
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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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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}]...
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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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2026-05-07T11:14:31.968680+00:00
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/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778152471968_m2.jpg...
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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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Claude is responding
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
Show more
10:42
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Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
Copy
Give positive feedback
Give negative feedback
You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
Edit
Copy
Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
Copy to clipboard
php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.
Copy
Give positive feedback
Give negative feedback
You said: Ok, so what will happen each time I make a request?
You said: Ok, so what will happen each time I make a request?
Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?
13:44
Edit
Copy
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Analyzed per-request overhead concerns and quantified Redis performance impact
Analyzed per-request overhead concerns and quantified Redis performance impact
Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out. But the overhead is much smaller than it sounds, and importantly, it's nowhere near your bottleneck.
What "two buckets per request" actually costs
What "two buckets per request" actually costs
Each request makes
one round trip to Redis
— not two — because you fold both bucket checks into a single Lua script that runs atomically server-side:
Copy to clipboard
lua
-- KEYS[1] = burst bucket key (or search)
-- KEYS[2] = daily bucket key
-- ARGV[1] = now (ms) ARGV[2] = window_ms
-- ARGV[3] = bucket_max ARGV[4] = daily_max
-- ARGV[5] = request_id ARGV[6] = daily_ttl
-- Trim sliding window
redis
.
call
(
'ZREMRANGEBYSCORE'
,
KEYS
[
1
]
,
0
,
ARGV
[
1
]
-
ARGV
[
2
]
)
local
burst_used
=
redis
.
call
(
'ZCARD'
,
KEYS
[
1
]
)
local
daily_used
=
tonumber
(
redis
.
call
(
'GET'
,
KEYS
[
2
]
)
or
'0'
)
if
burst_used
>=
tonumber
(
ARGV
[
3
]
)
then
-- Tell caller how long to sleep until oldest entry expires
local
oldest
=
redis
.
call
(
'ZRANGE'
,
KEYS
[
1
]
,...
|
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Laravel already speaks Redis natively, and you get atomic","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"INCR","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"EXPIRE","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"plus Lua scripting for true CAS semantics.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Key layout I'd use:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:search:{portalId} ZSET (sliding 1s)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:meta:{portalId} HASH (last seen headers)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The two short-window buckets are sliding-window-log (Redis ZSET, score =","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"microtime","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The daily bucket is a plain","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"INCR","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with TTL set to seconds-until-midnight in","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"HubSpot's account timezone","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to read it","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to read it","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Two-phase: optimistic pre-check, authoritative post-update.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Before a call, run the Lua acquire script. If it returns \"denied,\" sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"After the call, parse","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Max","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Interval-Milliseconds","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and store them in","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:meta:{portalId}","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"For search specifically, since headers don't come back, the local counter","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"is","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"not","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"on 429 (you really did make that request).","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to work with multiple jobs","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to work with multiple jobs","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Concurrency cap","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"via","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Queue::throttle()","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Per-tenant fairness","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Priority lanes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot-priority","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot-bulk","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Backoff on 429","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— respect the","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry-After","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"header. If absent, exponential with jitter (e.g.,","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"min(2^attempt * 250ms, 30s) ± 20%","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to work with paginated requests","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to work with paginated requests","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The trap is treating \"fetch all\" as one logical operation. Each page is its own API call and competes for tokens with everything else.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Two practical rules:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Don't hold the worker idle while paginating.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"processing","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"of page N as a separate job, and queue a \"fetch page N+1\" job with a delay equal to the wait time. Each page becomes its own atomic unit.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Always batch where HubSpot offers it.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/{type}/batch/{read|update|create}","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"accepts up to 100 IDs per call. For your \"patching IDs\" flow this is the difference between 600 calls and 6.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Walkthrough: 600 opportunities","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Walkthrough: 600 opportunities","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Sane flow:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Identify deltas","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— if you can avoid Search at all, do. Use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Batch read","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"current state —","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/deals/batch/read","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with 100 IDs per call → 6 calls. Burst weight 6.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Compute diff","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"in your code (no API).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Batch update","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"—","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/deals/batch/update","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with 100 per call → 6 calls.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Associations","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"—","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v4/associations/{from}/{to}/batch/create","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"if needed → another few batch calls.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Recognition: pre-check denies if","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"<weight>","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"slots aren't free; on 429 the response surfaces","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"policyName","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(DAILY/SECONDLY) — log it, push the job back with","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry-After","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"delay, increment a metric so you can alarm on sustained 429 rate.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Pitfalls & bottlenecks worth pre-empting","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Pitfalls & bottlenecks worth pre-empting","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Daily quota is shared across the whole portal","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search is uniquely fragile","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":": separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hs_object_id","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"ranges or by","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"windows.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry storms after HubSpot incidents","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Webhook → sync → webhook loops","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"jy_last_sync_id","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":") and ignore webhooks where it just changed.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Token refresh","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"expires_in - 60s","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", single-flight the refresh (only one worker refreshes; others wait on a lock).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search latency for fresh writes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Clock skew","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— Redis is authoritative. Don't use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"time()","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"from each worker for window scoring; use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"redis.call('TIME')","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"inside the Lua script.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Dead workers with held tokens","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The 5% error rule","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Phantom contention with Laravel","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"RateLimited","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"middleware","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":": it's per-job-class by default, not per-portal. If your","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"SyncDealJob","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"UpdateActivityJob","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"HubspotRateLimiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"service that everything routes through.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give positive feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give negative feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"You said: What will happen if there are two different types of rate limits?","depth":20,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"You said: What will happen if there are two different types of rate limits?","depth":21,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"What will happen if there are two different types of rate limits?","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search requests that have 5 requests per second","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"13:40","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Edit","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"Claude responded: They're independent buckets — with one shared bucket on top.","depth":21,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Claude responded: They're independent buckets — with one shared bucket on top.","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Analyzed independent rate limit buckets and their practical interactions","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Analyzed independent rate limit buckets and their practical interactions","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"They're independent buckets — with one shared bucket on top.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"The three buckets and what each call hits","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"The three buckets and what each call hits","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"┌──────────────────┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search request ──→ │ Search: 5/sec │ ──┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"└──────────────────┘ │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"├──→ ┌─────────────────┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"┌──────────────────┐ │ │ Daily: 250k–1M │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"└──────────────────┘ └─────────────────┘","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"What this means practically","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"What this means practically","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting search secondly limit (5/s):","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Only further","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/search","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting burst limit (190/10s):","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting daily limit:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"So can one type of request block the other?","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"So can one type of request block the other?","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search → Burst:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"No.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Burst → Search:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"No.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Saturating burst leaves Search's 5/s untouched.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Either → Daily:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Yes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", but only by contributing to the same daily counter. It's volume that kills you here, not the type.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"In theory you can sustain ~50 search + ~190 other =","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"~240 calls per 10 seconds","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"indefinitely (until daily runs out), because the two buckets bill independently.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Implementation consequence","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Implementation consequence","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"php","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'search'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// for search endpoints only","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'burst'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// for everything else","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'daily'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// every call, both kinds","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('search')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"+","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('daily')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". A batch update calls","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('burst')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"+","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('daily')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":".","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"One subtle pitfall: when you backoff on a 429,","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"inspect","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"policyName","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"in the response body","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to know which bucket actually fired. If you see","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"SECONDLY","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The other subtlety: the burst response gives you","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give positive feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give negative feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"You said: Ok, so what will happen each time I make a request?","depth":20,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"You said: Ok, so what will happen each time I make a request?","depth":21,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"13:44","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Edit","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.","depth":21,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Analyzed per-request overhead concerns and quantified Redis performance impact","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Analyzed per-request overhead concerns and quantified Redis performance impact","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out. But the overhead is much smaller than it sounds, and importantly, it's nowhere near your bottleneck.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"What \"two buckets per request\" actually costs","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"What \"two buckets per request\" actually costs","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Each request makes","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"one round trip to Redis","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— not two — because you fold both bucket checks into a single Lua script that runs atomically server-side:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"lua","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-- KEYS[1] = burst bucket key (or search)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-- KEYS[2] = daily bucket key","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-- ARGV[1] = now (ms) ARGV[2] = window_ms","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-- ARGV[3] = bucket_max ARGV[4] = daily_max","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-- ARGV[5] = request_id ARGV[6] = daily_ttl","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-- Trim sliding window","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"redis","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":".","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"call","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'ZREMRANGEBYSCORE'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"KEYS","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"[","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"]","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"0","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"ARGV","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"[","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"]","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"ARGV","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"[","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"2","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"]","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"local","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"burst_used","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"redis","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":".","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"call","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'ZCARD'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"KEYS","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"[","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"]","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"local","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"daily_used","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"tonumber","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"redis","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":".","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"call","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'GET'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_descriptio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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"}]...
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HubSpot rate limit implementation strategy, rename chat
HubSpot rate limit implementation strategy
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Claude is responding
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
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Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
Copy
Give positive feedback
Give negative feedback
You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
Edit
Copy
Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
Copy to clipboard
php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.
Copy
Give positive feedback
Give negative feedback
You said: Ok, so what will happen each time I make a request?
You said: Ok, so what will happen each time I make a request?
Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?
13:44
Edit
Copy
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Analyzed per-request overhead concerns and quantified Redis performance impact
Analyzed per-request overhead concerns and quantified Redis performance impact
Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out. But the overhead is much smaller than it sounds, and importantly, it's nowhere near your bottleneck.
What "two buckets per request" actually costs
What "two buckets per request" actually costs
Each request makes
one round trip to Redis
— not two — because you fold both bucket checks into a single Lua script that runs atomically server-side:
Copy to clipboard
lua
-- KEYS[1] = burst bucket key (or search)
-- KEYS[2] = daily bucket key
-- ARGV[1] = now (ms) ARGV[2] = window_ms
-- ARGV[3] = bucket_max ARGV[4] = daily_max
-- ARGV[5] = request_id ARGV[6] = daily_ttl
-- Trim sliding window
redis
.
call
(
'ZREMRANGEBYSCORE'
,
KEYS
[
1
]
,
0
,
ARGV
[
1
]
-
ARGV
[
2
]
)
local
burst_used
=
redis
.
call
(
'ZCARD'
,
KEYS
[
1
]
)
local
daily_used
=
tonumber
(
redis
.
call
(
'GET'
,
KEYS
[
2
]
)
or
'0'
)
if
burst_used
>=
tonumber
(
ARGV
[
3
]
)
then
-- Tell caller how long to sleep until oldest entry expires
local
oldest
=
redis
.
call
(
'ZRANGE'
,
KEYS
[
1
]
,...
|
2397
|
NULL
|
NULL
|
NULL
|
|
2399
|
104
|
9
|
2026-05-07T11:14:50.026822+00:00
|
/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778152490026_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'
,
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...
|
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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":"AXHeading","text":"You said: What will happen if there are two different types of rate limits?","depth":20,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"You said: What will happen if there are two different types of rate limits?","depth":21,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"What will happen if there are two different types of rate limits?","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search requests that have 5 requests per second","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"13:40","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Edit","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"Claude responded: They're independent buckets — with one shared bucket on top.","depth":21,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Claude responded: They're independent buckets — with one shared bucket on top.","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Analyzed independent rate limit buckets and their practical interactions","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Analyzed independent rate limit buckets and their practical interactions","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"They're independent buckets — with one shared bucket on top.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"The three buckets and what each call hits","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"The three buckets and what each call hits","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"┌──────────────────┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search request ──→ │ Search: 5/sec │ ──┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"└──────────────────┘ │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"├──→ ┌─────────────────┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"┌──────────────────┐ │ │ Daily: 250k–1M │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"└──────────────────┘ └─────────────────┘","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"What this means practically","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"What this means practically","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting search secondly limit (5/s):","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Only further","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/search","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting burst limit (190/10s):","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting daily limit:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"So can one type of request block the other?","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"So can one type of request block the other?","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search → Burst:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"No.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Burst → Search:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"No.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Saturating burst leaves Search's 5/s untouched.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Either → Daily:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Yes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", but only by contributing to the same daily counter. It's volume that kills you here, not the type.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"In theory you can sustain ~50 search + ~190 other =","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"~240 calls per 10 seconds","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"indefinitely (until daily runs out), because the two buckets bill independently.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Implementation consequence","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Implementation consequence","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"php","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'search'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// for search endpoints only","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'burst'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// for everything else","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'daily'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// every call, both kinds","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('search')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"+","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('daily')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". A batch update calls","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('burst')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"+","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('daily')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":".","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"One subtle pitfall: when you backoff on a 429,","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"inspect","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"policyName","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"in the response body","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to know which bucket actually fired. If you see","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"SECONDLY","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The other subtlety: the burst response gives you","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to clamp local state against, but search doesn't return those headers. 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HubSpot rate limit implementation strategy, rename chat
HubSpot rate limit implementation strategy
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Claude is responding
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
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Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
Copy
Give positive feedback
Give negative feedback
You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
Edit
Copy
Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
Copy to clipboard
php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.
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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'
,
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...
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|
/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778152496092_m2.jpg...
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Claude
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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
Show more
10:42
Retry
Edit
Copy
Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
Copy
Give positive feedback
Give negative feedback
Retry
You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
Retry
Edit
Copy
Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
Copy to clipboard
php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.
Copy
Give positive feedback
Give negative feedback
Retry
You said: Ok, so what will happen each time I make a request?
You said: Ok, so what will happen each time I make a request?
Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?
13:44
Retry
Edit
Copy
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Analyzed per-request overhead concerns and quantified Redis performance impact
Analyzed per-request overhead concerns and quantified Redis performance impact
Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out. But the overhead is much smaller than it sounds, and importantly, it's nowhere near your bottleneck.
What "two buckets per request" actually costs
What "two buckets per request" actually costs
Each request makes
one round trip to Redis
— not two — because you fold both bucket checks into a single Lua script that runs atomically server-side:
Copy to clipboard
lua
-- KEYS[1] = burst bucket key (or search)
-- KEYS[2] = daily bucket key
-- ARGV[1] = now (ms) ARGV[2] = window_ms
-- ARGV[3] = bucket_max ARGV[4] = daily_max
-- ARGV[5] = request_id ARGV[6] = daily_ttl
-- Trim sliding window
redis
.
call
(
'ZREMRANGEBYSCORE'
,
KEYS
[
1
]
,
0
,
ARGV
[
1
]
-
ARGV
[
2
]
)
local
burst_used
=
redis
.
call
(
'ZCARD'
,
KEYS
[
1
]
)
local
daily_used
=
tonumber
(
redis
.
call
(
'GET'
,
KEYS
[
2
]
)
or
'0'
)
if
burst_used
>=
tonumber
(
ARGV
[
3
]
)
then
-- Tell caller how long to sleep until oldest entry expires
local
oldest
=
redis
.
call
(
'ZRANGE'
,
KEYS
[
1
]
,
0
,
0
,
'WITHSCORES'
)
return
{
0
,
'BURST'
,
(
oldest
[
2
]
+
ARGV
[
2
]
)
-
ARGV
[
1
]
}
end
if
daily_used
>=
tonumber
(
ARGV
[
4
]
)
then
return
{
0
,
'DAILY'
,
-
1
}
end
redis
.
call
(
'ZADD'
,
KEYS...
|
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strategy","depth":21,"bounds":{"left":0.04454787,"top":0.031923383,"width":0.09507979,"height":0.014365523},"on_screen":true,"lines":[{"char_start":0,"char_count":1,"bounds":{"left":0.04454787,"top":0.031923383,"width":0.003656915,"height":0.014365523}},{"char_start":1,"char_count":41,"bounds":{"left":0.048204787,"top":0.031923383,"width":0.09142287,"height":0.014365523}}],"role_description":"text"},{"role":"AXPopUpButton","text":"More options for HubSpot rate limit implementation strategy","depth":19,"bounds":{"left":0.14128989,"top":0.02793296,"width":0.0066489363,"height":0.022346368},"on_screen":true,"role_description":"pop-up button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Share chat","depth":21,"bounds":{"left":0.48537233,"top":0.026336791,"width":0.010638298,"height":0.025538707},"on_screen":true,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Claude finished the response","depth":21,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"You said: So just a solution for rate limit implementation.","depth":20,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"You said: So just a solution for rate limit implementation.","depth":21,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"updating activity (engagement, meeting, call, note)","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"searching for a company or contact","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"updating deal","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"patching IDs\nReview what the limit is at the moment in the HubSpot documentation and then think of the solution:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"where to store it","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"how to read it","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"how to work with multiple jobs","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines","depth":25,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"). Example of such a error Client error:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST https://api.hubapi.com/crm/v3/objects/contact/search","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"resulted in a","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"429 Too Many Requests","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"response: {\"status\":\"error\",\"message\":\"You have reached your secondly limit.\",\"errorType\":\"RATE_LIMIT\",\"correlationId\":\"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Show more","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"10:42","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"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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Tell caller how long to sleep until oldest entry 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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
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Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
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You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
Retry
Edit
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Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
Copy to clipboard
php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.
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You said: Ok, so what will happen each time I make a request?
You said: Ok, so what will happen each time I make a request?
Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?
13:44
Retry
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...
|
2399
|
NULL
|
NULL
|
NULL
|
|
2402
|
104
|
11
|
2026-05-07T11:14:56.909658+00:00
|
/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778152496909_m2.jpg...
|
Claude
|
Claude
|
True
|
NULL
|
monitor_2
|
NULL
|
NULL
|
NULL
|
NULL
|
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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
Show more
10:42
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Copy
Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
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
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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...
|
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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":"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"}]...
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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
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10:42
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Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
Copy
Give positive feedback
Give negative feedback
Retry
You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
Retry
Edit
Copy
Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
Copy to clipboard
php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.
Copy
Give positive feedback
Give negative feedback
Retry
You said: Ok, so what will happen each time I make a request?
You said: Ok, so what will happen each time I make a request?
Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?
13:44
Retry
Edit
Copy
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Analyzed per-request overhead concerns and quantified Redis performance impact
Analyzed per-request overhead concerns and quantified Redis performance impact
Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out. But the overhead is much smaller than it sounds, and importantly, it's nowhere near your bottleneck.
What "two buckets per request" actually costs
What "two buckets per request" actually costs
Each request makes
one round trip to Redis
— not two — because you fold both bucket checks into a single Lua script that runs atomically server-side:
Copy to clipboard
lua
-- KEYS[1] = burst bucket key (or search)
-- KEYS[2] = daily bucket key...
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ClaudeFileVIewWindowHelp= HubSpot rate limit imple ClaudeFileVIewWindowHelp= HubSpot rate limit implementation strategy vI can see daily https://api.hubapi.com/account-info/v3/details what aboutsearch and burstDistinguished burst limits via headers from undocumented search constraints ›Burst: visible, but in response headers, not the body. Any non-search call worksn Postman. send.Reply +GET https://api.hubapi.com/account-info/v3/detailsAuthorization: Bearer (token}Then in the response panel click the Headers tab (not Body). You'll see something like:X-HubSpot-RateLimit-Max: 190X-HubSpot-RateLimit-Remaining: 189X-HubSpot-RateLimit-Interval-Milliseconds: 10000That tells you the portal is on 190 calls per 10 seconds. The Remaining value is live - fire thesame call again immediatelv 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 endnoint exnoses the S/see search imin• Search responses ( POST /crm/v3/objects/{type}/search ) deliberately strip all x-HubSnot-Ratel imit-* headers so vol can t read it from a search calll 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 vour tul ver-borta bicture from the APuisKeep going in Claude CodeSwitch to Claude Code and let Claude work directiv in vour reno..Write a message…Opus 4.7 Adaptive~Claude is Al and can make mistakes. Please double-check responses.40supoont Dally • In 40 m100% L2Inu / May 14.14:09UparadeQ Searchn. 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 ciGET Get EnaGET https://t•No environmentv~ SaveHTTP https:pi.hubapi.com/account-info/v3/api-usage/daily/private-appshttps://api.hubapi.com/account-info/v3/api-usage/daily/private-appsE DocsParams Authorization • Headers 9 Body Scripts SettinasAuth TypeBearer TokenTokenine autnorization neader will oe automaticallygenerated when you send the reauest. Learnmore about Bearer Token authorizationCookiesVariables in requestG token> All variablesCNeR-JHgMxIZQINQ…..((token))Body Cookies 1 Headers 20 lest ResultsJSONvPreview ?. Visualize v"results":"name": "private-aops-aoi-calls-daiilv".."ucadel imi+". 1000000"collectedAt": "2026-05-07T11:14:12.879Z".*2026-05-08T04:00:002*200 OK • 198 ms • 1.21 KB • (| .•=Q1OOGlobals Vault Tools S000...
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ClaudeFileVIewWindowHelp= HubSpot rate limit imple ClaudeFileVIewWindowHelp= HubSpot rate limit implementation strategy vI can see daily https://api.hubapi.com/account-info/v3/details what aboutsearch and burstDistinguished burst limits via headers from undocumented search constraints ›Burst: visible, but in response headers, not the body. Any non-search call worksn Postman. send.Reply +GET https://api.hubapi.com/account-info/v3/detailsAuthorization: Bearer (token}Then in the response panel click the Headers tab (not Body). You'll see something like:X-HubSpot-RateLimit-Max: 190X-HubSpot-RateLimit-Remaining: 189X-HubSpot-RateLimit-Interval-Milliseconds: 10000That tells you the portal is on 190 calls per 10 seconds. The Remaining value is live - fire thesame call again immediatelv 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 endnoint exnoses the S/see search imin• Search responses ( POST /crm/v3/objects/{type}/search ) deliberately strip all x-HubSnot-Ratel imit-* headers so vol can t read it from a search calll 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 vour tul ver-borta bicture from the APuisKeep going in Claude CodeSwitch to Claude Code and let Claude work directiv in vour reno..Write a message…Opus 4.7 Adaptive~Claude is Al and can make mistakes. Please double-check responses.40supoont Dally • In 40 m100% L2Inu / May 14.14:09UparadeQ Searchn. 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 ciGET Get EnaGET https://t•No environmentv~ SaveHTTP https:pi.hubapi.com/account-info/v3/api-usage/daily/private-appshttps://api.hubapi.com/account-info/v3/api-usage/daily/private-appsE DocsParams Authorization • Headers 9 Body Scripts SettinasAuth TypeBearer TokenTokenine autnorization neader will oe automaticallygenerated when you send the reauest. Learnmore about Bearer Token authorizationCookiesVariables in requestG token> All variablesCNeR-JHgMxIZQINQ…..((token))Body Cookies 1 Headers 20 lest ResultsJSONvPreview ?. Visualize v"results":"name": "private-aops-aoi-calls-daiilv".."ucadel imi+". 1000000"collectedAt": "2026-05-07T11:14:12.879Z".*2026-05-08T04:00:002*200 OK • 198 ms • 1.21 KB • (| .•=Q1OOGlobals Vault Tools S000...
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PostmanEditVIewWindowmelpHubSpot rate limit imple PostmanEditVIewWindowmelpHubSpot rate limit implementation strategyIcan see daily https:/api.hubapi.com/account=into/v3/details what aboutsearch and burstDistinguished burst limits via headers from undocumented search constraints ›Burst: visible. but in response headers, not the body. Any non-searen call worksn Postman. send.Reply -GET https://api.hubapi.com/account-info/v3/detailsAuthorization: Bearer {token}Then in the response panel click the Headers tab (not Body). You'll see something like:X-HubSpot-RateLimit-Max: 190X-HubSpot-RateLimit-Remaining: 189X-HubSpot-RateLimit-Interval-Milliseconds: 10000That tells you the portal is on 190 calls per 10 seconds. The Remaining value is live — fire thesame call again immediatelv 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 endnoint exnoses the s/see search limin• 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 vour original error.So vour tul ver-borta bicture from the APuisKeep going in Claude CodeSwitch to Claude Code and let Claude work directiv in vour reno..Write a message…Opus 4.7 Adaptive vhhlQ 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.HITP https://api.hubapi.com/account-info/v3/api-usage/daily/private-appshttps://api.hubapi.com/account-info/v3/api-usage/daily/private-apps= DocsParamsAuthorization • Headers 9 Body ScriptsSettinasAuth TypeTokenine autnorization neader will oe automaticallygenerated when you send the reauest. Learnmore about Bearer Token authorizationGET httos://:{tokenl}"suppont Dally • In 40m100% L2Thu 7 May 14:15:02No environmentv~ Savecookies4* AIVariables in requestG token> All variablesCOLLECTIONScontacts› CRM ObjectseRM owners> CRM PipelinesDealsEngagements• D OLD ENGAGEMENTSGET list meetingsPST search moditied companiePOST search tasksGET read call> Post search callsGET list callsPOST meetings scheduledGET aet meetinaPOST get link to task> POST Create Contact with Associatio> HubsnotJournal & webhoooks v4POSt Get tokenGET get subscriptionsPOST create subscrintionGET Journal earliestGET lournal latestGeT https:ubspot-webhooks-lournal-nal.s.GET neyt offsetpost get loken prodDEL DELSTE CURSCRIPTION DEP PORTAL.GET DEAL WITH HISTORY pRODEptIcC vA©Authi> Properties> RESSARCHwCCADAUPOST search contact bv ohonePOSt search contact by emailPOST search meetinasPOST search notes> Post Search calls v3.IPOST Search related meetinas v3POST search dealsCAMIDONMCNTC> SPFCSELOWSConnect Git E Console 2 TerminCNeR-JHaMxlZoiNd.BOdVJSONvPreviewp. Visualize v"results":"name": "private-aops-aoi-calls-dailv"."ucadel imi+". 1000000"collectedAt": "2026-05-07T11:14:12.879Z""2026-05-08T04:00:002200 OK • 198 ms • 1.21 KB • (| .•=Q 8Globals Vault Tools?000...
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PostmanEditVIewWindowmelpHubSpot rate limit imple PostmanEditVIewWindowmelpHubSpot rate limit implementation strategyIcan see daily https:/api.hubapi.com/account=into/v3/details what aboutsearch and burstDistinguished burst limits via headers from undocumented search constraints ›Burst: visible. but in response headers, not the body. Any non-searen call worksn Postman. send.Reply -GET https://api.hubapi.com/account-info/v3/detailsAuthorization: Bearer {token}Then in the response panel click the Headers tab (not Body). You'll see something like:X-HubSpot-RateLimit-Max: 190X-HubSpot-RateLimit-Remaining: 189X-HubSpot-RateLimit-Interval-Milliseconds: 10000That tells you the portal is on 190 calls per 10 seconds. The Remaining value is live — fire thesame call again immediatelv 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 endnoint exnoses the s/see search limin• 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 vour original error.So vour tul ver-borta bicture from the APuisKeep going in Claude CodeSwitch to Claude Code and let Claude work directiv in vour reno..Write a message…Opus 4.7 Adaptive vhhlQ 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.HITP https://api.hubapi.com/account-info/v3/api-usage/daily/private-appshttps://api.hubapi.com/account-info/v3/api-usage/daily/private-apps= DocsParamsAuthorization • Headers 9 Body ScriptsSettinasAuth TypeTokenine autnorization neader will oe automaticallygenerated when you send the reauest. Learnmore about Bearer Token authorizationGET httos://:{tokenl}"suppont Dally • In 40m100% L2Thu 7 May 14:15:02No environmentv~ Savecookies4* AIVariables in requestG token> All variablesCOLLECTIONScontacts› CRM ObjectseRM owners> CRM PipelinesDealsEngagements• D OLD ENGAGEMENTSGET list meetingsPST search moditied companiePOST search tasksGET read call> Post search callsGET list callsPOST meetings scheduledGET aet meetinaPOST get link to task> POST Create Contact with Associatio> HubsnotJournal & webhoooks v4POSt Get tokenGET get subscriptionsPOST create subscrintionGET Journal earliestGET lournal latestGeT https:ubspot-webhooks-lournal-nal.s.GET neyt offsetpost get loken prodDEL DELSTE CURSCRIPTION DEP PORTAL.GET DEAL WITH HISTORY pRODEptIcC vA©Authi> Properties> RESSARCHwCCADAUPOST search contact bv ohonePOSt search contact by emailPOST search meetinasPOST search notes> Post Search calls v3.IPOST Search related meetinas v3POST search dealsCAMIDONMCNTC> SPFCSELOWSConnect Git E Console 2 TerminCNeR-JHaMxlZoiNd.BOdVJSONvPreviewp. Visualize v"results":"name": "private-aops-aoi-calls-dailv"."ucadel imi+". 1000000"collectedAt": "2026-05-07T11:14:12.879Z""2026-05-08T04:00:002200 OK • 198 ms • 1.21 KB • (| .•=Q 8Globals Vault Tools?000...
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PostmanEditVIewWindowmelpHubSpot rate limit imple PostmanEditVIewWindowmelpHubSpot rate limit implementation strategyIcan see daily https:/api.hubapi.com/account=into/v3/details what aboutsearch and burstDistinguished burst limits via headers from undocumented search constraints ›Burst: visible. but in response headers, not the body. Any non-searen call worksn Postman. send.Reply -GET https://api.hubapi.com/account-info/v3/detailsAuthorization: Bearer {token}Then in the response panel click the Headers tab (not Body). You'll see something like:X-HubSpot-RateLimit-Max: 190X-HubSpot-RateLimit-Remaining: 189X-HubSpot-RateLimit-Interval-Milliseconds: 10000That tells you the portal is on 190 calls per 10 seconds. The Remaining value is live — fire thesame call again immediatelv 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 endnoint exnoses the s/see search limin• 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 vour original error.So vour tul ver-borta bicture from the APuisKeep going in Claude CodeSwitch to Claude Code and let Claude work directiv in vour reno..Write a message…Opus 4.7 Adaptive vInu / May 14.10.00UparadeQ 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.https:///api.hubapi.com/account-info/v3/detailshttps://api.hubapi.com/account-info/v3/details= DocsParamsAuthorization • Headers 9 Body ScriptsSettinasAuth TypeTokenine autnorization neader will oe automaticallygenerated when you send the reauest. Learnmore about Bearer Token authorization{tokenl}GET httos://:"suppont Dally • In 40mNo environmentva Save1º0d ~ Cookies100% L24* AIVariables in requestG token› All VarlablesCOLLECTIONScontacts› CRM ObjectseRM owners> CRM Pipelines> DealsEngagements• D OLD ENGAGEMENTSGET list meetingsPST search moditied companiePOST search tasksGET read call> Post search callsGET list callsPOST meetings scheduledGET aet meetinaPOST get link to task> POST Create Contact with Associatio> HubsnotJournal & webhoooks v4POSt Get tokenGET get subscriptionsPOST create subscrintionGET Journal earliestGET lournal latesGeT https:ubspot-webhooks-lournal-nal.s.GET neyt offcotpost get loken prodDEL DELSTE CURSCRIPTION DEP PORTAL.GET DEAL WITH HISTORY pRODEptIcC vA©Authi> Properties> RESSARCHwCCADAUPOST search contact bv ohonePOST search contact by emailPOST search meetinasPOST search notes> Post Search calls v3.IPOST Search related meetinas v3POST search dealsCAMIDONMCNTC> SPFCSELOWSConnect Git E Console 2 TerminCNeR-JHaMxlZoiNd.BodyJSONvPreviewp. Visualize v"results":"name": "private-aops-aoi-calls-dailv"."ucadel imi+". 1000000"collectedAt": "2026-05-07T11:14:12.879Z""2026-05-08T04:00:002200 OK • 198 ms • 1.21 KB • (| .•=Q 8Globals Vault Tools?000...
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PostmanEditVIewWindowmelpHubSpot rate limit imple PostmanEditVIewWindowmelpHubSpot rate limit implementation strategyIcan see daily https:/api.hubapi.com/account=into/v3/details what aboutsearch and burstDistinguished burst limits via headers from undocumented search constraints ›Burst: visible. but in response headers, not the body. Any non-searen call worksn Postman. send.Reply -GET https://api.hubapi.com/account-info/v3/detailsAuthorization: Bearer {token}Then in the response panel click the Headers tab (not Body). You'll see something like:X-HubSpot-RateLimit-Max: 190X-HubSpot-RateLimit-Remaining: 189X-HubSpot-RateLimit-Interval-Milliseconds: 10000That tells you the portal is on 190 calls per 10 seconds. The Remaining value is live — fire thesame call again immediatelv 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 endnoint exnoses the s/see search limin• 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 vour original error.So vour tul ver-borta bicture from the APuisKeep going in Claude CodeSwitch to Claude Code and let Claude work directiv in vour reno..Write a message…Opus 4.7 Adaptive vInu / May 14.10.00UparadeQ 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.https:///api.hubapi.com/account-info/v3/detailshttps://api.hubapi.com/account-info/v3/details= DocsParamsAuthorization • Headers 9 Body ScriptsSettinasAuth TypeTokenine autnorization neader will oe automaticallygenerated when you send the reauest. Learnmore about Bearer Token authorization{tokenl}GET httos://:"suppont Dally • In 40mNo environmentva Save1º0d ~ Cookies100% L24* AIVariables in requestG token› All VarlablesCOLLECTIONScontacts› CRM ObjectseRM owners> CRM Pipelines> DealsEngagements• D OLD ENGAGEMENTSGET list meetingsPST search moditied companiePOST search tasksGET read call> Post search callsGET list callsPOST meetings scheduledGET aet meetinaPOST get link to task> POST Create Contact with Associatio> HubsnotJournal & webhoooks v4POSt Get tokenGET get subscriptionsPOST create subscrintionGET Journal earliestGET lournal latesGeT https:ubspot-webhooks-lournal-nal.s.GET neyt offcotpost get loken prodDEL DELSTE CURSCRIPTION DEP PORTAL.GET DEAL WITH HISTORY pRODEptIcC vA©Authi> Properties> RESSARCHwCCADAUPOST search contact bv ohonePOST search contact by emailPOST search meetinasPOST search notes> Post Search calls v3.IPOST Search related meetinas v3POST search dealsCAMIDONMCNTC> SPFCSELOWSConnect Git E Console 2 TerminCNeR-JHaMxlZoiNd.BodyJSONvPreviewp. Visualize v"results":"name": "private-aops-aoi-calls-dailv"."ucadel imi+". 1000000"collectedAt": "2026-05-07T11:14:12.879Z""2026-05-08T04:00:002200 OK • 198 ms • 1.21 KB • (| .•=Q 8Globals Vault Tools?000...
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2026-05-07T11:15:17.501423+00:00
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/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778152517501_m2.jpg...
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PostmanEditVIewWindowhelpHubSpot rate limit imple PostmanEditVIewWindowhelpHubSpot rate limit implementation strategyIcan see daily https:/api.hubapi.com/account=into/v3/details what aboutsearch and burstDistinguished burst limits via headers from undocumented search constraints ›Burst: visible. but in response headers, not the body. Any non-searen call worksn Postman. send.Reply -GET https://api.hubapi.com/account-info/v3/detailsAuthorization: Bearer {token}Then in the response panel click the Headers tab (not Body). You'll see something like:X-HubSpot-RateLimit-Max: 190X-HubSpot-RateLimit-Remaining: 189X-HubSpot-RateLimit-Interval-Milliseconds: 10000That tells you the portal is on 190 calls per 10 seconds. The Remaining value is live — fire thesame call again immediatelv 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 endnoint exnoses the s/see search limin• 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 vour original error.So vour tul ver-borta bicture from the APuisKeep going in Claude CodeSwitch to Claude Code and let Claude work directiv in vour reno..Write a message…Opus 4.7 Adaptive 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. •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 httos://:"suppont Dally • In 40mNo environmentv~ Save100% L24* AIVariables in requestG tokenCookies› All VarlablesThu 7 May 14:15:17UparadeCNeR-JHaMxlZoiNd.v COLLECtIONscontacts› CRM ObjectseRM ownersCRM PioelinecDealsEngagements• D OLD ENGAGEMENTSGET list meetingsPOST search moditied companiesPOST search tasksGET read call> Post search callsGET list callsPOST meetings scheduledGET aet meetinaPOST get link to task> POST Create Contact with Associatio> HubsnotJournal & webhoooks v4POSt Get tokenGET det subscriptionsPOST create subscrintionGET Journal earliestGET Journal latestGeT https:ubspot-webhooks-lournal-nal.s.GET neyt offsetpost get loken prodDEL DELETE SUBSCRIPTION PER PORTALGET DEAL WITH HISTORY pRODEptIcC vA©Authi> Properties> RESSARCHwCCADAUPOST search contact bv ohonePOSt search contact by emailPOST search meetingsPOST search notes> Post Search calls v3.IPOST Search related meetinas v3POST search dealsCAMIDONMCNTC> SPFCSELOWSConnect Git E Console 2 Termirggers 20200 ok • 209ms • 1.24 KB • CA **•Statusdatecontent-typecf-rayct-cache-statusstrict-transport-securityaccess-control-allow-credentialsserver-uimingx-hubspot-correlation.idy-hubenot.ratolimit.intorval-millicocondcx-hubspot-ratelimit-remainingychubsnot-ratelimit-cocondlvreport-toThu. 07 Mav 2026 11:15:08 GMTIapplication/json;charset=utf-89f7fce1hd89dhda6-sofDYNAMICmax-age=31536000; includeSubDomains: preloadorigin, Accept-Encodingfalseheididace-"01000226.0dha-7012-052h-2072211/0762" Afridace-"0f76c01h011d2402-1A0%nosnift019e0226-0dba-7a1a-853b-a97a211c97f210000("endpoints":[{"url":"https://a.nel.cloudflare.comVreport\v4?s=%2B8ZxycfkqBM4vc64itFbi.f"quececs fraction".0.01 "renort to"."ef-nel""may aae".604900}cloudflareGlobals Vault Tools?000...
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PostmanEditVIewWindowhelpHubSpot rate limit imple PostmanEditVIewWindowhelpHubSpot rate limit implementation strategyIcan see daily https:/api.hubapi.com/account=into/v3/details what aboutsearch and burstDistinguished burst limits via headers from undocumented search constraints ›Burst: visible. but in response headers, not the body. Any non-searen call worksn Postman. send.Reply -GET https://api.hubapi.com/account-info/v3/detailsAuthorization: Bearer {token}Then in the response panel click the Headers tab (not Body). You'll see something like:X-HubSpot-RateLimit-Max: 190X-HubSpot-RateLimit-Remaining: 189X-HubSpot-RateLimit-Interval-Milliseconds: 10000That tells you the portal is on 190 calls per 10 seconds. The Remaining value is live — fire thesame call again immediatelv 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 endnoint exnoses the s/see search limin• 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 vour original error.So vour tul ver-borta bicture from the APuisKeep going in Claude CodeSwitch to Claude Code and let Claude work directiv in vour reno..Write a message…Opus 4.7 Adaptive 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. •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 httos://:"suppont Dally • In 40mNo environmentv~ Save100% L24* AIVariables in requestG tokenCookies› All VarlablesThu 7 May 14:15:17UparadeCNeR-JHaMxlZoiNd.v COLLECtIONscontacts› CRM ObjectseRM ownersCRM PioelinecDealsEngagements• D OLD ENGAGEMENTSGET list meetingsPOST search moditied companiesPOST search tasksGET read call> Post search callsGET list callsPOST meetings scheduledGET aet meetinaPOST get link to task> POST Create Contact with Associatio> HubsnotJournal & webhoooks v4POSt Get tokenGET det subscriptionsPOST create subscrintionGET Journal earliestGET Journal latestGeT https:ubspot-webhooks-lournal-nal.s.GET neyt offsetpost get loken prodDEL DELETE SUBSCRIPTION PER PORTALGET DEAL WITH HISTORY pRODEptIcC vA©Authi> Properties> RESSARCHwCCADAUPOST search contact bv ohonePOSt search contact by emailPOST search meetingsPOST search notes> Post Search calls v3.IPOST Search related meetinas v3POST search dealsCAMIDONMCNTC> SPFCSELOWSConnect Git E Console 2 Termirggers 20200 ok • 209ms • 1.24 KB • CA **•Statusdatecontent-typecf-rayct-cache-statusstrict-transport-securityaccess-control-allow-credentialsserver-uimingx-hubspot-correlation.idy-hubenot.ratolimit.intorval-millicocondcx-hubspot-ratelimit-remainingychubsnot-ratelimit-cocondlvreport-toThu. 07 Mav 2026 11:15:08 GMTIapplication/json;charset=utf-89f7fce1hd89dhda6-sofDYNAMICmax-age=31536000; includeSubDomains: preloadorigin, Accept-Encodingfalseheididace-"01000226.0dha-7012-052h-2072211/0762" Afridace-"0f76c01h011d2402-1A0%nosnift019e0226-0dba-7a1a-853b-a97a211c97f210000("endpoints":[{"url":"https://a.nel.cloudflare.comVreport\v4?s=%2B8ZxycfkqBM4vc64itFbi.f"quececs fraction".0.01 "renort to"."ef-nel""may aae".604900}cloudflareGlobals Vault Tools?000...
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2026-05-07T11:15:20.395630+00:00
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/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778152520395_m2.jpg...
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PostmanEditVIewWindowmelpHubSpot rate limit imple PostmanEditVIewWindowmelpHubSpot rate limit implementation strategyIcan see daily https:/api.hubapi.com/account=into/v3/details what aboutsearch and burstDistinguished burst limits via headers from undocumented search constraints ›Burst: visible. but in response headers, not the body. Any non-searen call worksn Postman. send.Reply -GET https://api.hubapi.com/account-info/v3/detailsAuthorization: Bearer {token}Then in the response panel click the Headers tab (not Body). You'll see something like:X-HubSpot-RateLimit-Max: 190X-HubSpot-RateLimit-Remaining: 189X-HubSpot-RateLimit-Interval-Milliseconds: 10000That tells you the portal is on 190 calls per 10 seconds. The Remaining value is live — fire thesame call again immediatelv 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 endnoint exnoses the S/see search imin• 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 vour original error.So vour tul ver-borta bicture from the APuisKeep going in Claude CodeSwitch to Claude Code and let Claude work directiv in vour reno..Write a message…Opus 4.7 Adaptive 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.GET read ciGET httos://: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."suppont Dally • In 40mNo environmentv~ SaveCookies100% L24* AIVariables in requestG token› All VarlablesThu 7 May 14:15:20UparadeCNeR-JHaMxlZoiNd.v COLLECtIONscontacts› CRM ObjectseRM ownersCRM Pioelinec> DealsEngagements• D OLD ENGAGEMENTSGET list meetingsPOST search moditied companiesPOST search tasksGET read call> Post search callsGET list callsPOST meetings scheduledGET aet meetinaPOST get link to task> POST Create Contact with Associatio> HubsnotJournal & webhoooks v4POSt Get tokenGET det subscriptionsPOST create subscrintionGET Journal earliestGET Journal latestGET https:ubspot-webhooks-lournal-nal.s.GET neyt offsetpost get loken prodDEL DELETE SUBSCRIPTION PER PORTALGET DEAL WITH HISTORY pRODEpTICC VA©Authi> Properties> RESSARCHwCCADAUPOSt search contact bv ohonePOST search contact by emailPOST search meetingsPOST search notes> Post Search calls v3.IPOST Search related meetinas v3POST search dealsCAMIDONMCNTC> SPFCSELOWSConnect Git E Console 2 TerminBOdVHeaders 20 lest Resultsistatuscontent-tvoecf-rayef-cache-statusstrict-transport-securityaccess-control-allow-credentialsserver-timingyacontent-tune.ontionsx-hubspot-correlation-idw.hubenot.rotolimit.intorvol.milliconondewwlnerrwoltmeohanhy-hubsnot-ratelimit-secondlv-remainingreport-toservecontent-encodind200 OK • 209 ms • 1.24 KB • (| .•Thu. 07 Mav 2026 11:15:08 GMTIapplication/ison:charset=utf-89f7fce1hd89dhda6.sopDYNAMICmax-age=31536000; includeSubDomains; preloadincludesubDomains: preloadhcid desc="019e0226-0dba-7a1a-853b-a97a211c97f2" cfr-desc="9f7fce1be11d3402-IAD"nosniff019e0226-0dba-7a1a-853b-a97a211c972f"endpoints":[("url":*httos:Wa.nel.cloudflare.comVreportVv4?s=%2B8ZxvcfkqBM4vc64itFbi.f"cuecoss fraction".0.01 "ronort to"."af_nelkimay aao".604900}cloudflareGlobals Vault Tools?000...
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-955148318946156266
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PostmanEditVIewWindowmelpHubSpot rate limit imple PostmanEditVIewWindowmelpHubSpot rate limit implementation strategyIcan see daily https:/api.hubapi.com/account=into/v3/details what aboutsearch and burstDistinguished burst limits via headers from undocumented search constraints ›Burst: visible. but in response headers, not the body. Any non-searen call worksn Postman. send.Reply -GET https://api.hubapi.com/account-info/v3/detailsAuthorization: Bearer {token}Then in the response panel click the Headers tab (not Body). You'll see something like:X-HubSpot-RateLimit-Max: 190X-HubSpot-RateLimit-Remaining: 189X-HubSpot-RateLimit-Interval-Milliseconds: 10000That tells you the portal is on 190 calls per 10 seconds. The Remaining value is live — fire thesame call again immediatelv 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 endnoint exnoses the S/see search imin• 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 vour original error.So vour tul ver-borta bicture from the APuisKeep going in Claude CodeSwitch to Claude Code and let Claude work directiv in vour reno..Write a message…Opus 4.7 Adaptive 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.GET read ciGET httos://: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."suppont Dally • In 40mNo environmentv~ SaveCookies100% L24* AIVariables in requestG token› All VarlablesThu 7 May 14:15:20UparadeCNeR-JHaMxlZoiNd.v COLLECtIONscontacts› CRM ObjectseRM ownersCRM Pioelinec> DealsEngagements• D OLD ENGAGEMENTSGET list meetingsPOST search moditied companiesPOST search tasksGET read call> Post search callsGET list callsPOST meetings scheduledGET aet meetinaPOST get link to task> POST Create Contact with Associatio> HubsnotJournal & webhoooks v4POSt Get tokenGET det subscriptionsPOST create subscrintionGET Journal earliestGET Journal latestGET https:ubspot-webhooks-lournal-nal.s.GET neyt offsetpost get loken prodDEL DELETE SUBSCRIPTION PER PORTALGET DEAL WITH HISTORY pRODEpTICC VA©Authi> Properties> RESSARCHwCCADAUPOSt search contact bv ohonePOST search contact by emailPOST search meetingsPOST search notes> Post Search calls v3.IPOST Search related meetinas v3POST search dealsCAMIDONMCNTC> SPFCSELOWSConnect Git E Console 2 TerminBOdVHeaders 20 lest Resultsistatuscontent-tvoecf-rayef-cache-statusstrict-transport-securityaccess-control-allow-credentialsserver-timingyacontent-tune.ontionsx-hubspot-correlation-idw.hubenot.rotolimit.intorvol.milliconondewwlnerrwoltmeohanhy-hubsnot-ratelimit-secondlv-remainingreport-toservecontent-encodind200 OK • 209 ms • 1.24 KB • (| .•Thu. 07 Mav 2026 11:15:08 GMTIapplication/ison:charset=utf-89f7fce1hd89dhda6.sopDYNAMICmax-age=31536000; includeSubDomains; preloadincludesubDomains: preloadhcid desc="019e0226-0dba-7a1a-853b-a97a211c97f2" cfr-desc="9f7fce1be11d3402-IAD"nosniff019e0226-0dba-7a1a-853b-a97a211c972f"endpoints":[("url":*httos:Wa.nel.cloudflare.comVreportVv4?s=%2B8ZxvcfkqBM4vc64itFbi.f"cuecoss fraction".0.01 "ronort to"."af_nelkimay aao".604900}cloudflareGlobals Vault Tools?000...
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2026-05-07T11:15:51.524068+00:00
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/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778152551524_m2.jpg...
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iTerm2
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PostmanEditVIewWindowhelpHubSpot rate limit imple PostmanEditVIewWindowhelpHubSpot rate limit implementation strategyIcan see daily https:/api.hubapi.com/account=into/v3/details what aboutsearch and burstDistinguished burst limits via headers from undocumented search constraints ›Burst: visible. but in response headers, not the body. Any non-searen call worksn Postman. send.Reply -GET https://api.hubapi.com/account-info/v3/detailsAuthorization: Bearer {token}Then in the response panel click the Headers tab (not Body). You'll see something like:X-HubSpot-RateLimit-Max: 190X-HubSpot-RateLimit-Remaining: 189X-HubSpot-RateLimit-Interval-Milliseconds: 10000That tells you the portal is on 190 calls per 10 seconds. The Remaining value is live — fire thesame call again immediatelv 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 endnoint exnoses the s/see search limin• 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 vour original error.So vour tul ver-borta bicture from the APuisKeep going in Claude CodeSwitch to Claude Code and let Claude work directiv in vour reno..Write a message…Opus 4.7 Adaptive vClaude ic Aland can mako mistakes Plesce double-chock racnoncac)Thu 7 May 14:15:51UparadeQ 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.GET read ciGET httos://:HTTP https:pi.hubapi.com/account-info/v3/detailsnttps://api.nubapi.com/accoun-info/vs/details= DocsAuthorization • Headers 9 Body ScriptsSettinasAuth TypeTokenine autnorization neader will oe automaticallygenerated when you send the reauest. Learnmore about Bearer Token authorization.suppont Dally • In 40mNo environmentv~ SaveCookies100% L24* AIVariables in requestG token› All VarlablesCOLLECTIONScontacts› CRM ObjectseRM ownersCRM Pioelinec> DealsEngagements• D OLD ENGAGEMENTSGET list meetingsPOST search moditied companiesPOST search tasksGET read call> PosT search callsGET list callsPOST meetings scheduledGET aet meetinaPOST get link to task> POST Create Contact with Associatio> HubsnotJournal & webhoooks v4POSt Get tokenGET det subscriptionsPOST create subscrintionGET Journal earliestGET Journal latestGET https:ubspot-webhooks-lournal-nal.s.GET neyt offsetpost get loken prodDEL DELETE SUBSCRIPTION PER PORTALGET DEAL WITH HISTORY pRODEpTIcC vA©Authi> Properties> RESSARCHwCCADAUPOST search contact bv ohonePOST search contact by emailPOST search meetingsPOST search notes> Post Search calls v3.POST Search related meetinas v3POST search dealsCAMIDONMCNTC> SPFCSELOWSConnect Git E Console 2 TermirCNeR-JHaMxlZoiNd.Cookiesistatusdatecontent-tvoecf-rayef-cache-statusstrict-transport-securityaccess-control-allow-credentialsserver-timingyacontent-tune.ontionsx-hubspot-correlation-idw.hubenot.rotolimit.intorvol.milliconandewwlnerrwolmteohhnay-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-Encodingtalsehcid:desc="019e0226-0dba-7a1a-853b-a97a211c97f2" cfr'desc="9f7fce1be11d3402-IAD"nosniff019e0226-0dba-7a1a-853b-a97a211c976210000"endpoints":""url":httos:Va.nel.cloudflare.com/report/v4?s=%288ZxvcfkaBM4vc64itFbi.f"cuecoss fraction".0.01 "ronort to"."of_nolkimay aag".604900}cloudflareGlobals Vault Tools?000...
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-2132253344044793983
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NULL
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idle
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ocr
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NULL
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PostmanEditVIewWindowhelpHubSpot rate limit imple PostmanEditVIewWindowhelpHubSpot rate limit implementation strategyIcan see daily https:/api.hubapi.com/account=into/v3/details what aboutsearch and burstDistinguished burst limits via headers from undocumented search constraints ›Burst: visible. but in response headers, not the body. Any non-searen call worksn Postman. send.Reply -GET https://api.hubapi.com/account-info/v3/detailsAuthorization: Bearer {token}Then in the response panel click the Headers tab (not Body). You'll see something like:X-HubSpot-RateLimit-Max: 190X-HubSpot-RateLimit-Remaining: 189X-HubSpot-RateLimit-Interval-Milliseconds: 10000That tells you the portal is on 190 calls per 10 seconds. The Remaining value is live — fire thesame call again immediatelv 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 endnoint exnoses the s/see search limin• 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 vour original error.So vour tul ver-borta bicture from the APuisKeep going in Claude CodeSwitch to Claude Code and let Claude work directiv in vour reno..Write a message…Opus 4.7 Adaptive vClaude ic Aland can mako mistakes Plesce double-chock racnoncac)Thu 7 May 14:15:51UparadeQ 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.GET read ciGET httos://:HTTP https:pi.hubapi.com/account-info/v3/detailsnttps://api.nubapi.com/accoun-info/vs/details= DocsAuthorization • Headers 9 Body ScriptsSettinasAuth TypeTokenine autnorization neader will oe automaticallygenerated when you send the reauest. Learnmore about Bearer Token authorization.suppont Dally • In 40mNo environmentv~ SaveCookies100% L24* AIVariables in requestG token› All VarlablesCOLLECTIONScontacts› CRM ObjectseRM ownersCRM Pioelinec> DealsEngagements• D OLD ENGAGEMENTSGET list meetingsPOST search moditied companiesPOST search tasksGET read call> PosT search callsGET list callsPOST meetings scheduledGET aet meetinaPOST get link to task> POST Create Contact with Associatio> HubsnotJournal & webhoooks v4POSt Get tokenGET det subscriptionsPOST create subscrintionGET Journal earliestGET Journal latestGET https:ubspot-webhooks-lournal-nal.s.GET neyt offsetpost get loken prodDEL DELETE SUBSCRIPTION PER PORTALGET DEAL WITH HISTORY pRODEpTIcC vA©Authi> Properties> RESSARCHwCCADAUPOST search contact bv ohonePOST search contact by emailPOST search meetingsPOST search notes> Post Search calls v3.POST Search related meetinas v3POST search dealsCAMIDONMCNTC> SPFCSELOWSConnect Git E Console 2 TermirCNeR-JHaMxlZoiNd.Cookiesistatusdatecontent-tvoecf-rayef-cache-statusstrict-transport-securityaccess-control-allow-credentialsserver-timingyacontent-tune.ontionsx-hubspot-correlation-idw.hubenot.rotolimit.intorvol.milliconandewwlnerrwolmteohhnay-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-Encodingtalsehcid:desc="019e0226-0dba-7a1a-853b-a97a211c97f2" cfr'desc="9f7fce1be11d3402-IAD"nosniff019e0226-0dba-7a1a-853b-a97a211c976210000"endpoints":""url":httos:Va.nel.cloudflare.com/report/v4?s=%288ZxvcfkaBM4vc64itFbi.f"cuecoss fraction".0.01 "ronort to"."of_nolkimay aag".604900}cloudflareGlobals Vault Tools?000...
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2416
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104
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18
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2026-05-07T11:15:53.041428+00:00
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/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778152553041_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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NULL
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PostmanEditVIewWindowhelpHubSpot rate limit imple PostmanEditVIewWindowhelpHubSpot rate limit implementation strategyIcan see daily https:/api.hubapi.com/account=into/v3/details what aboutsearch and burstDistinguished burst limits via headers from undocumented search constraints ›Burst: visible. but in response headers, not the body. Any non-searen call worksn Postman. send.Reply -GET https://api.hubapi.com/account-info/v3/detailsAuthorization: Bearer {token}Then in the response panel click the Headers tab (not Body). You'll see something like:X-HubSpot-RateLimit-Max: 190X-HubSpot-RateLimit-Remaining: 189X-HubSpot-RateLimit-Interval-Milliseconds: 10000That tells you the portal is on 190 calls per 10 seconds. The Remaining value is live — fire thesame call again immediatelv 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 endnoint exnoses the s/see search limin• 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 vour original error.So vour tul ver-borta bicture from the APuisKeep going in Claude CodeSwitch to Claude Code and let Claude work directiv in vour reno..Write a message…Opus 4.7 Adaptive vClaude ic Aland can mako mistakes Plesce double-chock racnoncac)suppont Dally • In 40mQ 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.COLLECTIONScontacts› CRM ObjectseRM ownersCRM Pioelinec> DealsEngagements• D OLD ENGAGEMENTSGET list meetingsPOST search moditied companiesPOST search tasksGET read call> PosT search callsGET list callsPOST meetings scheduledGET aet meetinaPOST get link to task> POST Create Contact with Associatio> HubsnotJournal & webhoooks v4POSt Get tokenGET det subscriptionsPOST create subscrintionGET Journal earliestGET Journal latestGET https:ubspot-webhooks-lournal-nal.s.GET neyt offsetpost get loken prodDEL DELETE SUBSCRIPTION PER PORTALGET DEAL WITH HISTORY pRODEpTIcC vA©Authi> Properties> RESSARCHwCCADAUPOST search contact bv ohonePOST search contact by emailPOST search meetingsPOST search notes> Post Search calls v3.POST Search related meetinas v3POST search dealsCAMIDONMCNTC> SPFCSELOWSConnect Git E Console 2 TermirGET next off. •GET read ciGET httos://:No environmentvHTTP https:pi.hubapi.com/account-info/v3/details~ Saventtps://api.nubapi.com/accoun-info/vs/details= DocsAuthorization • Headers 9 Body ScriptsSettinasCookiesAuth TypeTokenine autnorization neader will oe automaticallygenerated when you send the reauest. Learnmore about Bearer Token authorization.615%Body Cookiesistatuscontent-tvoecf-rayef-cache-statusstrict-transport-securityaccess-control-allow-credentialsserver-timingyacontent-tune.ontionsx-hubspot-correlation-idw.hubenot.rotolimit.intorvol.milliconandewwlnerrwolmteohhnay-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-Encodingtalsehcid:desc="019e0226-0dba-7a1a-853b-a97a211c97f2" cfr'desc="9f7fce1be11d3402-IAD"nosniff019e0226-0dba-7a1a-853b-a97a211c976210000"endpoints":""url":httos:Va.nel.cloudflare.com/report/v4?s=%288ZxvcfkaBM4vc64itFbi.f"cuecoss fraction".0.01 "ronort to"."of_nolkimay aag".604900}cloudflare100% L24* AIVariables in requestG token› All VarlablesInu / May 14:10.02UparadeCNeR-JHaMxlZoiNd.Globals Vault Tools?000...
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NULL
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5320359198465850742
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click
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ocr
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PostmanEditVIewWindowhelpHubSpot rate limit imple PostmanEditVIewWindowhelpHubSpot rate limit implementation strategyIcan see daily https:/api.hubapi.com/account=into/v3/details what aboutsearch and burstDistinguished burst limits via headers from undocumented search constraints ›Burst: visible. but in response headers, not the body. Any non-searen call worksn Postman. send.Reply -GET https://api.hubapi.com/account-info/v3/detailsAuthorization: Bearer {token}Then in the response panel click the Headers tab (not Body). You'll see something like:X-HubSpot-RateLimit-Max: 190X-HubSpot-RateLimit-Remaining: 189X-HubSpot-RateLimit-Interval-Milliseconds: 10000That tells you the portal is on 190 calls per 10 seconds. The Remaining value is live — fire thesame call again immediatelv 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 endnoint exnoses the s/see search limin• 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 vour original error.So vour tul ver-borta bicture from the APuisKeep going in Claude CodeSwitch to Claude Code and let Claude work directiv in vour reno..Write a message…Opus 4.7 Adaptive vClaude ic Aland can mako mistakes Plesce double-chock racnoncac)suppont Dally • In 40mQ 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.COLLECTIONScontacts› CRM ObjectseRM ownersCRM Pioelinec> DealsEngagements• D OLD ENGAGEMENTSGET list meetingsPOST search moditied companiesPOST search tasksGET read call> PosT search callsGET list callsPOST meetings scheduledGET aet meetinaPOST get link to task> POST Create Contact with Associatio> HubsnotJournal & webhoooks v4POSt Get tokenGET det subscriptionsPOST create subscrintionGET Journal earliestGET Journal latestGET https:ubspot-webhooks-lournal-nal.s.GET neyt offsetpost get loken prodDEL DELETE SUBSCRIPTION PER PORTALGET DEAL WITH HISTORY pRODEpTIcC vA©Authi> Properties> RESSARCHwCCADAUPOST search contact bv ohonePOST search contact by emailPOST search meetingsPOST search notes> Post Search calls v3.POST Search related meetinas v3POST search dealsCAMIDONMCNTC> SPFCSELOWSConnect Git E Console 2 TermirGET next off. •GET read ciGET httos://:No environmentvHTTP https:pi.hubapi.com/account-info/v3/details~ Saventtps://api.nubapi.com/accoun-info/vs/details= DocsAuthorization • Headers 9 Body ScriptsSettinasCookiesAuth TypeTokenine autnorization neader will oe automaticallygenerated when you send the reauest. Learnmore about Bearer Token authorization.615%Body Cookiesistatuscontent-tvoecf-rayef-cache-statusstrict-transport-securityaccess-control-allow-credentialsserver-timingyacontent-tune.ontionsx-hubspot-correlation-idw.hubenot.rotolimit.intorvol.milliconandewwlnerrwolmteohhnay-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-Encodingtalsehcid:desc="019e0226-0dba-7a1a-853b-a97a211c97f2" cfr'desc="9f7fce1be11d3402-IAD"nosniff019e0226-0dba-7a1a-853b-a97a211c976210000"endpoints":""url":httos:Va.nel.cloudflare.com/report/v4?s=%288ZxvcfkaBM4vc64itFbi.f"cuecoss fraction".0.01 "ronort to"."of_nolkimay aag".604900}cloudflare100% L24* AIVariables in requestG token› All VarlablesInu / May 14:10.02UparadeCNeR-JHaMxlZoiNd.Globals Vault Tools?000...
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2026-05-07T11:15:54.353368+00:00
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/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778152554353_m2.jpg...
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iTerm2
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PostmanEditVIewWindowmelpHubSpot rate limit imple PostmanEditVIewWindowmelpHubSpot rate limit implementation strategyIcan see daily https:/api.hubapi.com/account=into/v3/details what aboutsearch and burstDistinguished burst limits via headers from undocumented search constraints ›Burst: visible. but in response headers, not the body. Any non-searen call worksn Postman. send.Reply -GET https://api.hubapi.com/account-info/v3/detailsAuthorization: Bearer {token}Then in the response panel click the Headers tab (not Body). You'll see something like:X-HubSpot-RateLimit-Max: 190X-HubSpot-RateLimit-Remaining: 189X-HubSpot-RateLimit-Interval-Milliseconds: 10000That tells you the portal is on 190 calls per 10 seconds. The Remaining value is live — fire thesame call again immediatelv 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 endnoint exnoses the s/see search limin• 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 vour original error.So vour tul ver-borta bicture from the APuisKeep going in Claude CodeSwitch to Claude Code and let Claude work directiv in vour reno..Write a message…Opus 4.7 Adaptive vClaude ic Aland can mako mistakes Plesce double-chock racnoncac)Thu 7 May 14:15:54Q 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. •HTTP https:pi.hubapi.com/account-info/v3/detailsnttps://api.nubapi.com/accoun-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 httos://:suppont Dally • In 40mNo environmentv~ Save100% L24* AIVariables in requestCookies› All Varlablesv COLLECtIONscontacts› CRM ObjectseRM ownersCRM Pioelinec> DealsEngagements• D OLD ENGAGEMENTSGET list meetingsPOST search moditied companiesPOST search tasksGET read call> PosT search callsGET list callsPOST meetings scheduledGET aet meetinaPOST get link to task> POST Create Contact with Associatio> HubsnotJournal & webhoooks v4POSt Get tokenGET det subscriptionsPOST create subscrintionGET Journal earliestGET Journal latestGET https:ubspot-webhooks-lournal-nal.s.GET neyt offcotpost get loken prodDEL DELETE SUBSCRIPTION PER PORTALGET DEAL WITH HISTORY pRODEpTIcC vA©Authi> Properties> RESSARCHwCCADAUPOST search contact bv ohonePOSt search contact by emailPOST search meetingsPOST search notes> Post Search calls v3.POST Search related meetinas v3POST search dealsCAMIDONMCNTC> SPFCSELOWSConnect Git E Console 2 TermirCNeR-JHaMxlZoiNd.Body CookiesHeaders 20 Test Resultsistatusdatecontent-tvoecf-rayef-cache-statusstrict-transport-securityaccess-control-allow-credentialsserver-timingX-hubspot-correlation-idMewlhncnalerwoltratetmborweltmnilterermewwlnenrollmtcohhnaservecontent-encodind200 OK 209 ms • 1.24 KEThu. 07 May 2026 11:15:08 GMTIapplication/ison:charset=utf-89f7fce1hd89dbda6-sopDYNAMICmax-age=31536000; includesubDomains: preloadorigin, Accept-Encodingralsehcid:desc="019e0226-0dba-7a1a-853b-a97a211c97f2". cfr'desc="9f7fce1be11d3402-IAD"019e0226-0dba-7a1a-853b-a97a211c971210000f"endpoints":[("url":*httos:Wa.nel.cloudflare.comVreportVv4?s=%2B8ZxvcfkqBM4vc64itFbi.("success_fraction":0.01,"report_to":"cf-nel""max_age":604800}cloudflare197AGlobals Vault Tools?000...
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visual_change
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ocr
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NULL
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PostmanEditVIewWindowmelpHubSpot rate limit imple PostmanEditVIewWindowmelpHubSpot rate limit implementation strategyIcan see daily https:/api.hubapi.com/account=into/v3/details what aboutsearch and burstDistinguished burst limits via headers from undocumented search constraints ›Burst: visible. but in response headers, not the body. Any non-searen call worksn Postman. send.Reply -GET https://api.hubapi.com/account-info/v3/detailsAuthorization: Bearer {token}Then in the response panel click the Headers tab (not Body). You'll see something like:X-HubSpot-RateLimit-Max: 190X-HubSpot-RateLimit-Remaining: 189X-HubSpot-RateLimit-Interval-Milliseconds: 10000That tells you the portal is on 190 calls per 10 seconds. The Remaining value is live — fire thesame call again immediatelv 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 endnoint exnoses the s/see search limin• 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 vour original error.So vour tul ver-borta bicture from the APuisKeep going in Claude CodeSwitch to Claude Code and let Claude work directiv in vour reno..Write a message…Opus 4.7 Adaptive vClaude ic Aland can mako mistakes Plesce double-chock racnoncac)Thu 7 May 14:15:54Q 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. •HTTP https:pi.hubapi.com/account-info/v3/detailsnttps://api.nubapi.com/accoun-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 httos://:suppont Dally • In 40mNo environmentv~ Save100% L24* AIVariables in requestCookies› All Varlablesv COLLECtIONscontacts› CRM ObjectseRM ownersCRM Pioelinec> DealsEngagements• D OLD ENGAGEMENTSGET list meetingsPOST search moditied companiesPOST search tasksGET read call> PosT search callsGET list callsPOST meetings scheduledGET aet meetinaPOST get link to task> POST Create Contact with Associatio> HubsnotJournal & webhoooks v4POSt Get tokenGET det subscriptionsPOST create subscrintionGET Journal earliestGET Journal latestGET https:ubspot-webhooks-lournal-nal.s.GET neyt offcotpost get loken prodDEL DELETE SUBSCRIPTION PER PORTALGET DEAL WITH HISTORY pRODEpTIcC vA©Authi> Properties> RESSARCHwCCADAUPOST search contact bv ohonePOSt search contact by emailPOST search meetingsPOST search notes> Post Search calls v3.POST Search related meetinas v3POST search dealsCAMIDONMCNTC> SPFCSELOWSConnect Git E Console 2 TermirCNeR-JHaMxlZoiNd.Body CookiesHeaders 20 Test Resultsistatusdatecontent-tvoecf-rayef-cache-statusstrict-transport-securityaccess-control-allow-credentialsserver-timingX-hubspot-correlation-idMewlhncnalerwoltratetmborweltmnilterermewwlnenrollmtcohhnaservecontent-encodind200 OK 209 ms • 1.24 KEThu. 07 May 2026 11:15:08 GMTIapplication/ison:charset=utf-89f7fce1hd89dbda6-sopDYNAMICmax-age=31536000; includesubDomains: preloadorigin, Accept-Encodingralsehcid:desc="019e0226-0dba-7a1a-853b-a97a211c97f2". cfr'desc="9f7fce1be11d3402-IAD"019e0226-0dba-7a1a-853b-a97a211c971210000f"endpoints":[("url":*httos:Wa.nel.cloudflare.comVreportVv4?s=%2B8ZxvcfkqBM4vc64itFbi.("success_fraction":0.01,"report_to":"cf-nel""max_age":604800}cloudflare197AGlobals Vault Tools?000...
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2026-05-07T11:15:56.347114+00:00
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/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778152556347_m2.jpg...
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Claude
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Claude
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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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Bulgarian citizenship application process for EU residents
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HubSpot rate limit implementation strategy
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HubSpot rate limit implementation strategy, rename chat
HubSpot rate limit implementation strategy
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Claude 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?...
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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"}]...
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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
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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...
|
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Laravel already speaks Redis natively, and you get atomic","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"INCR","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"EXPIRE","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"plus Lua scripting for true CAS semantics.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Key layout I'd use:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:search:{portalId} ZSET (sliding 1s)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:meta:{portalId} HASH (last seen headers)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The two short-window buckets are sliding-window-log (Redis ZSET, score =","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"microtime","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The daily bucket is a plain","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"INCR","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with TTL set to seconds-until-midnight in","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"HubSpot's account timezone","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to read it","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to read it","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Two-phase: optimistic pre-check, authoritative post-update.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Before a call, run the Lua acquire script. If it returns \"denied,\" sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"After the call, parse","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Max","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Interval-Milliseconds","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and store them in","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:meta:{portalId}","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"For search specifically, since headers don't come back, the local counter","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"is","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"not","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"on 429 (you really did make that request).","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to work with multiple jobs","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to work with multiple jobs","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Concurrency cap","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"via","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Queue::throttle()","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Per-tenant fairness","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Priority lanes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot-priority","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot-bulk","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Backoff on 429","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— respect the","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry-After","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"header. If absent, exponential with jitter (e.g.,","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"min(2^attempt * 250ms, 30s) ± 20%","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to work with paginated requests","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to work with paginated requests","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The trap is treating \"fetch all\" as one logical operation. Each page is its own API call and competes for tokens with everything else.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Two practical rules:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Don't hold the worker idle while paginating.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"processing","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"of page N as a separate job, and queue a \"fetch page N+1\" job with a delay equal to the wait time. Each page becomes its own atomic unit.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Always batch where HubSpot offers it.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/{type}/batch/{read|update|create}","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"accepts up to 100 IDs per call. For your \"patching IDs\" flow this is the difference between 600 calls and 6.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Walkthrough: 600 opportunities","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Walkthrough: 600 opportunities","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Sane flow:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Identify deltas","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— if you can avoid Search at all, do. Use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Batch read","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"current state —","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/deals/batch/read","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with 100 IDs per call → 6 calls. Burst weight 6.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Compute diff","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"in your code (no API).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Batch update","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"—","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/deals/batch/update","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with 100 per call → 6 calls.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Associations","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"—","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v4/associations/{from}/{to}/batch/create","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"if needed → another few batch calls.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Recognition: pre-check denies if","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"<weight>","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"slots aren't free; on 429 the response surfaces","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"policyName","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(DAILY/SECONDLY) — log it, push the job back with","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry-After","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"delay, increment a metric so you can alarm on sustained 429 rate.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Pitfalls & bottlenecks worth pre-empting","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Pitfalls & bottlenecks worth pre-empting","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Daily quota is shared across the whole portal","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search is uniquely fragile","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":": separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hs_object_id","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"ranges or by","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"windows.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry storms after HubSpot incidents","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Webhook → sync → webhook loops","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"jy_last_sync_id","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":") and ignore webhooks where it just changed.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Token refresh","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"expires_in - 60s","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", single-flight the refresh (only one worker refreshes; others wait on a lock).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search latency for fresh writes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Clock skew","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— Redis is authoritative. Don't use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"time()","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"from each worker for window scoring; use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"redis.call('TIME')","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"inside the Lua script.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Dead workers with held tokens","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The 5% error rule","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Phantom contention with Laravel","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"RateLimited","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"middleware","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":": it's per-job-class by default, not per-portal. If your","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"SyncDealJob","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"UpdateActivityJob","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"HubspotRateLimiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"service that everything routes through.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give positive feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give negative feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"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. 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n":"text"},{"role":"AXStaticText","text":"KEYS","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"[","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"2","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"]","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"or","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'0'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"if","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"burst_used","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":">=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"tonumber","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"ARGV","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"[","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"3","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"]","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"then","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-- Tell caller how long to sleep until oldest entry expires","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"local","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"oldest","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"redis","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":".","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"call","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'ZRANGE'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"KEYS","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"[","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"]","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"0","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"0","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'WITHSCORES'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"return","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"{","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"0","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'BURST'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"oldest","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"[","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"2","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"]","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"+","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"ARGV","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"[","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"2","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"]","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"ARGV","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"[","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"]","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"}","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"end","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"if","depth":27,"on_screen":false,"role_description":"text"}]...
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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
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Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
Copy to clipboard
php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.
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You said: Ok, so what will happen each time I make a request?
You said: Ok, so what will happen each time I make a request?
Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?
13:44
Retry
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Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Analyzed per-request overhead concerns and quantified Redis performance impact
Analyzed per-request overhead concerns and quantified Redis performance impact
Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out. But the overhead is much smaller than it sounds, and importantly, it's nowhere near your bottleneck.
What "two buckets per request" actually costs
What "two buckets per request" actually costs
Each request makes
one round trip to Redis
— not two — because you fold both bucket checks into a single Lua script that runs atomically server-side:
Copy to clipboard
lua
-- 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...
|
NULL
|
NULL
|
NULL
|
NULL
|
|
2421
|
104
|
22
|
2026-05-07T11:16:03.547358+00:00
|
/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778152563547_m2.jpg...
|
Claude
|
Claude
|
True
|
NULL
|
monitor_2
|
NULL
|
NULL
|
NULL
|
NULL
|
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Open sidebar
Claude is responding
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
Show more
10:42
Edit
Copy
Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
Copy
Give positive feedback
Give negative feedback
You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
Edit
Copy
Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
Copy to clipboard
php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
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'
,...
|
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collapse","depth":16,"bounds":{"left":0.10239362,"top":0.06703911,"width":0.030585106,"height":0.011971269},"on_screen":true,"lines":[{"char_start":0,"char_count":1,"bounds":{"left":0.10239362,"top":0.06703911,"width":0.0029920214,"height":0.011971269}},{"char_start":1,"char_count":16,"bounds":{"left":0.10538564,"top":0.06703911,"width":0.027925532,"height":0.011971269}}],"role_description":"text"},{"role":"AXStaticText","text":"⌘B","depth":16,"bounds":{"left":0.1349734,"top":0.06703911,"width":0.0063164895,"height":0.011971269},"on_screen":true,"role_description":"text"},{"role":"AXStaticText","text":"Drag to 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The daily headers are not returned for OAuth-authenticated calls","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— that's enforced for marketplace listing, but it's also a sane operational target.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"So you have","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"four buckets","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Where to store it","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Where to store it","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"INCR","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"EXPIRE","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"plus Lua scripting for true CAS semantics.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Key layout I'd use:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:search:{portalId} ZSET (sliding 1s)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:meta:{portalId} HASH (last seen headers)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The two short-window buckets are sliding-window-log (Redis ZSET, score =","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"microtime","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The daily bucket is a plain","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"INCR","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with TTL set to seconds-until-midnight in","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"HubSpot's account timezone","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to read it","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to read it","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Two-phase: optimistic pre-check, authoritative post-update.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Before a call, run the Lua acquire script. If it returns \"denied,\" sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"After the call, parse","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Max","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Interval-Milliseconds","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and store them in","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:meta:{portalId}","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"For search specifically, since headers don't come back, the local counter","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"is","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"not","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"on 429 (you really did make that request).","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to work with multiple jobs","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to work with multiple jobs","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Concurrency cap","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"via","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Queue::throttle()","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Per-tenant fairness","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Priority lanes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot-priority","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot-bulk","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Backoff on 429","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— respect the","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry-After","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"header. If absent, exponential with jitter (e.g.,","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"min(2^attempt * 250ms, 30s) ± 20%","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to work with paginated requests","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to work with paginated requests","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The trap is treating \"fetch all\" as one logical operation. Each page is its own API call and competes for tokens with everything else.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Two practical rules:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Don't hold the worker idle while paginating.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"processing","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"of page N as a separate job, and queue a \"fetch page N+1\" job with a delay equal to the wait time. Each page becomes its own atomic unit.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Always batch where HubSpot offers it.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/{type}/batch/{read|update|create}","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"accepts up to 100 IDs per call. For your \"patching IDs\" flow this is the difference between 600 calls and 6.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Walkthrough: 600 opportunities","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Walkthrough: 600 opportunities","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Sane flow:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Identify deltas","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— if you can avoid Search at all, do. Use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Batch read","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"current state —","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/deals/batch/read","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with 100 IDs per call → 6 calls. Burst weight 6.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Compute diff","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"in your code (no API).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Batch update","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"—","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/deals/batch/update","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with 100 per call → 6 calls.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Associations","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"—","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v4/associations/{from}/{to}/batch/create","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"if needed → another few batch calls.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Recognition: pre-check denies if","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"<weight>","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"slots aren't free; on 429 the response surfaces","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"policyName","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(DAILY/SECONDLY) — log it, push the job back with","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry-After","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"delay, increment a metric so you can alarm on sustained 429 rate.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Pitfalls & bottlenecks worth pre-empting","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Pitfalls & bottlenecks worth pre-empting","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Daily quota is shared across the whole portal","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search is uniquely fragile","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":": separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hs_object_id","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"ranges or by","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"windows.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry storms after HubSpot incidents","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Webhook → sync → webhook loops","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"jy_last_sync_id","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":") and ignore webhooks where it just changed.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Token refresh","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"expires_in - 60s","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", single-flight the refresh (only one worker refreshes; others wait on a lock).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search latency for fresh writes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Clock skew","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— Redis is authoritative. Don't use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"time()","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"from each worker for window scoring; use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"redis.call('TIME')","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"inside the Lua script.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Dead workers with held tokens","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The 5% error rule","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Phantom contention with Laravel","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"RateLimited","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"middleware","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":": it's per-job-class by default, not per-portal. If your","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"SyncDealJob","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"UpdateActivityJob","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"HubspotRateLimiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"service that everything routes through.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give positive feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give negative feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"You said: What will happen if there are two different types of rate limits?","depth":20,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"You said: What will happen if there are two different types of rate limits?","depth":21,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"What will happen if there are two different types of rate limits?","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search requests that have 5 requests per second","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"13:40","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Edit","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"Claude responded: They're independent buckets — with one shared bucket on top.","depth":21,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Claude responded: They're independent buckets — with one shared bucket on top.","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Analyzed independent rate limit buckets and their practical interactions","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Analyzed independent rate limit buckets and their practical interactions","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"They're independent buckets — with one shared bucket on top.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"The three buckets and what each call hits","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"The three buckets and what each call hits","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"┌──────────────────┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search request ──→ │ Search: 5/sec │ ──┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"└──────────────────┘ │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"├──→ ┌─────────────────┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"┌──────────────────┐ │ │ Daily: 250k–1M │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"└──────────────────┘ └─────────────────┘","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"What this means practically","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"What this means practically","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting search secondly limit (5/s):","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Only further","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/search","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. 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Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? 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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":"}","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"end","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"if","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"daily_used","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":">=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"tonumber","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"ARGV","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"[","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"4","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"]","depth":27,"on_screen":false,"role_description":"text"},{"role":"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":"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":"'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"}]...
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Claude is responding
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
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10:42
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Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
Copy
Give positive feedback
Give negative feedback
You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
Edit
Copy
Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
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'
,...
|
2420
|
NULL
|
NULL
|
NULL
|
|
2422
|
104
|
23
|
2026-05-07T11:16:24.679008+00:00
|
/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778152584679_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...
|
[{"role":"AXLink","text":& [{"role":"AXLink","text":"Skip to content","depth":14,"bounds":{"left":0.029587766,"top":0.03830806,"width":0.0003324468,"height":0.0007980846},"on_screen":true,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Skip to content","depth":15,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Click to collapse","depth":16,"bounds":{"left":0.10239362,"top":0.06703911,"width":0.030585106,"height":0.011971269},"on_screen":true,"lines":[{"char_start":0,"char_count":1,"bounds":{"left":0.10239362,"top":0.06703911,"width":0.0029920214,"height":0.011971269}},{"char_start":1,"char_count":16,"bounds":{"left":0.10538564,"top":0.06703911,"width":0.027925532,"height":0.011971269}}],"role_description":"text"},{"role":"AXStaticText","text":"⌘B","depth":16,"bounds":{"left":0.1349734,"top":0.06703911,"width":0.0063164895,"height":0.011971269},"on_screen":true,"role_description":"text"},{"role":"AXStaticText","text":"Drag to resize","depth":16,"bounds":{"left":0.10239362,"top":0.079010375,"width":0.025930852,"height":0.011971269},"on_screen":true,"lines":[{"char_start":0,"char_count":1,"bounds":{"left":0.10239362,"top":0.079010375,"width":0.0029920214,"height":0.011971269}},{"char_start":1,"char_count":13,"bounds":{"left":0.10538564,"top":0.079010375,"width":0.022938829,"height":0.011971269}}],"role_description":"text"},{"role":"AXButton","text":"Open 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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. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"What this means practically","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"What this means practically","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting search secondly limit (5/s):","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Only further","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/search","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting burst limit (190/10s):","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting daily limit:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"So can one type of request block the other?","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"So can one type of request block the other?","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search → Burst:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"No.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Burst → Search:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"No.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Saturating burst leaves Search's 5/s untouched.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Either → Daily:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Yes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", but only by contributing to the same daily counter. It's volume that kills you here, not the type.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"In theory you can sustain ~50 search + ~190 other =","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"~240 calls per 10 seconds","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"indefinitely (until daily runs out), because the two buckets bill independently.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Implementation consequence","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Implementation consequence","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"php","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'search'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// for search endpoints only","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'burst'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// for everything else","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'daily'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// every call, both kinds","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('search')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"+","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('daily')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". A batch update calls","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('burst')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"+","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('daily')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":".","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"One subtle pitfall: when you backoff on a 429,","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"inspect","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"policyName","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"in the response body","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to know which bucket actually fired. If you see","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"SECONDLY","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The other subtlety: the burst response gives you","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give positive feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give negative feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"You said: Ok, so what will happen each time I make a request?","depth":20,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"You said: Ok, so what will happen each time I make a request?","depth":21,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"13:44","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Edit","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.","depth":21,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Analyzed per-request overhead concerns and quantified Redis performance impact","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Analyzed per-request overhead concerns and quantified Redis performance impact","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out. But the overhead is much smaller than it sounds, and importantly, it's nowhere near your bottleneck.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"What \"two buckets per request\" actually costs","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"What \"two buckets per request\" actually costs","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Each request makes","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"one round trip to Redis","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— not two — because you fold both bucket checks into a single Lua script that runs atomically server-side:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"lua","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-- KEYS[1] = burst bucket key (or search)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-- KEYS[2] = daily bucket key","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-- ARGV[1] = now (ms) ARGV[2] = window_ms","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-- ARGV[3] = bucket_max ARGV[4] = daily_max","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-- ARGV[5] = request_id ARGV[6] = daily_ttl","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-- Trim sliding window","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"redis","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":".","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"call","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'ZREMRANGEBYSCORE'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"KEYS","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"[","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"]","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"0","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"ARGV","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"[","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"]","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"ARGV","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"[","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"2","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"]","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"local","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"burst_used","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"redis","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":".","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"call","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'ZCARD'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"KEYS","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"[","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"}]...
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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...
|
NULL
|
NULL
|
NULL
|
NULL
|
|
2424
|
104
|
24
|
2026-05-07T11:16:36.227054+00:00
|
/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778152596227_m2.jpg...
|
Claude
|
Claude
|
True
|
NULL
|
monitor_2
|
NULL
|
NULL
|
NULL
|
NULL
|
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Open sidebar
Claude is responding
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
Show more
10:42
Edit
Copy
Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
Copy
Give positive feedback
Give negative feedback
You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
Edit
Copy
Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
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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...
|
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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":"Edit","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"Claude responded: Current HubSpot limits (Apr 2026)","depth":21,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Claude responded: Current HubSpot limits (Apr 2026)","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Current HubSpot limits (Apr 2026)","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Current HubSpot limits (Apr 2026)","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". The burst window is per app; the daily quota is per portal.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". That's the limit the 429 in your example is hitting (","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"secondly limit","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"on","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/crm/v3/objects/contact/search","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"). It's a separate bucket from the 190/10s, and critically, search responses do not include the","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-*","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"headers","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— you can't read remaining capacity off the response, you have to track it yourself.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Non-search endpoints return","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Max","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", and","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Interval-Milliseconds","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". The daily headers are not returned for OAuth-authenticated calls","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— that's enforced for marketplace listing, but it's also a sane operational target.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"So you have","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"four buckets","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Where to store it","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Where to store it","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"INCR","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"EXPIRE","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"plus Lua scripting for true CAS semantics.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Key layout I'd use:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:search:{portalId} ZSET (sliding 1s)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:meta:{portalId} HASH (last seen headers)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The two short-window buckets are sliding-window-log (Redis ZSET, score =","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"microtime","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The daily bucket is a plain","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"INCR","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with TTL set to seconds-until-midnight in","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"HubSpot's account timezone","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to read it","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to read it","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Two-phase: optimistic pre-check, authoritative post-update.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Before a call, run the Lua acquire script. If it returns \"denied,\" sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"After the call, parse","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Max","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Interval-Milliseconds","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and store them in","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:meta:{portalId}","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"For search specifically, since headers don't come back, the local counter","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"is","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"not","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"on 429 (you really did make that request).","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to work with multiple jobs","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to work with multiple jobs","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Concurrency cap","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"via","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Queue::throttle()","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Per-tenant fairness","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Priority lanes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot-priority","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot-bulk","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Backoff on 429","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— respect the","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry-After","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"header. If absent, exponential with jitter (e.g.,","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"min(2^attempt * 250ms, 30s) ± 20%","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to work with paginated requests","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to work with paginated requests","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The trap is treating \"fetch all\" as one logical operation. Each page is its own API call and competes for tokens with everything else.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Two practical rules:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Don't hold the worker idle while paginating.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"processing","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"of page N as a separate job, and queue a \"fetch page N+1\" job with a delay equal to the wait time. Each page becomes its own atomic unit.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Always batch where HubSpot offers it.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/{type}/batch/{read|update|create}","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"accepts up to 100 IDs per call. For your \"patching IDs\" flow this is the difference between 600 calls and 6.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Walkthrough: 600 opportunities","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Walkthrough: 600 opportunities","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Sane flow:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Identify deltas","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— if you can avoid Search at all, do. Use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Batch read","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"current state —","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/deals/batch/read","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with 100 IDs per call → 6 calls. Burst weight 6.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Compute diff","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"in your code (no API).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Batch update","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"—","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/deals/batch/update","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with 100 per call → 6 calls.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Associations","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"—","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v4/associations/{from}/{to}/batch/create","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"if needed → another few batch calls.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Recognition: pre-check denies if","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"<weight>","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"slots aren't free; on 429 the response surfaces","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"policyName","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(DAILY/SECONDLY) — log it, push the job back with","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry-After","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"delay, increment a metric so you can alarm on sustained 429 rate.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Pitfalls & bottlenecks worth pre-empting","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Pitfalls & bottlenecks worth pre-empting","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Daily quota is shared across the whole portal","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search is uniquely fragile","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":": separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hs_object_id","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"ranges or by","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"windows.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry storms after HubSpot incidents","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Webhook → sync → webhook loops","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"jy_last_sync_id","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":") and ignore webhooks where it just changed.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Token refresh","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"expires_in - 60s","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", single-flight the refresh (only one worker refreshes; others wait on a lock).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search latency for fresh writes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Clock skew","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— Redis is authoritative. Don't use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"time()","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"from each worker for window scoring; use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"redis.call('TIME')","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"inside the Lua script.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Dead workers with held tokens","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The 5% error rule","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Phantom contention with Laravel","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"RateLimited","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"middleware","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":": it's per-job-class by default, not per-portal. If your","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"SyncDealJob","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"UpdateActivityJob","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"HubspotRateLimiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"service that everything routes through.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give positive feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give negative feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"You said: What will happen if there are two different types of rate limits?","depth":20,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"You said: What will happen if there are two different types of rate limits?","depth":21,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"What will happen if there are two different types of rate limits?","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search requests that have 5 requests per second","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"13:40","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Edit","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"Claude responded: They're independent buckets — with one shared bucket on top.","depth":21,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Claude responded: They're independent buckets — with one shared bucket on top.","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Analyzed independent rate limit buckets and their practical interactions","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Analyzed independent rate limit buckets and their practical interactions","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"They're independent buckets — with one shared bucket on top.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"The three buckets and what each call hits","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"The three buckets and what each call hits","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"┌──────────────────┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search request ──→ │ Search: 5/sec │ ──┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"└──────────────────┘ │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"├──→ ┌─────────────────┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"┌──────────────────┐ │ │ Daily: 250k–1M │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"└──────────────────┘ └─────────────────┘","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"What this means practically","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"What this means practically","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting search secondly limit (5/s):","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Only further","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/search","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting burst limit (190/10s):","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting daily limit:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"So can one type of request block the other?","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"So can one type of request block the other?","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search → Burst:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"No.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Burst → Search:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"No.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Saturating burst leaves Search's 5/s untouched.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Either → Daily:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Yes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", but only by contributing to the same daily counter. It's volume that kills you here, not the type.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"In theory you can sustain ~50 search + ~190 other =","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"~240 calls per 10 seconds","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"indefinitely (until daily runs out), because the two buckets bill independently.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Implementation consequence","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Implementation consequence","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"php","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'search'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// for search endpoints only","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'burst'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// for everything else","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'daily'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// every call, both kinds","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('search')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"+","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('daily')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". A batch update calls","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('burst')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"+","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('daily')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":".","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"One subtle pitfall: when you backoff on a 429,","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"inspect","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"policyName","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"in the response body","depth":26,"on_screen":false,"role_description":"text"}]...
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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...
|
2422
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NULL
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NULL
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NULL
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|
2427
|
104
|
25
|
2026-05-07T11:16:37.609542+00:00
|
/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778152597609_m2.jpg...
|
Claude
|
Claude
|
True
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NULL
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monitor_2
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NULL
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NULL
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NULL
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NULL
|
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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...
|
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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. 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}]...
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Claude is responding
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
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10:42
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Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
Copy
Give positive feedback
Give negative feedback
You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
Edit
Copy
Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
Copy to clipboard
php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.
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/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778152628092_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
[...
|
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That's the limit the 429 in your example is hitting (","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"secondly limit","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"on","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/crm/v3/objects/contact/search","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"). It's a separate bucket from the 190/10s, and critically, search responses do not include the","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-*","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"headers","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— you can't read remaining capacity off the response, you have to track it yourself.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Non-search endpoints return","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Max","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", and","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Interval-Milliseconds","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". The daily headers are not returned for OAuth-authenticated calls","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— that's enforced for marketplace listing, but it's also a sane operational target.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"So you have","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"four buckets","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Where to store it","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Where to store it","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"INCR","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"EXPIRE","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"plus Lua scripting for true CAS semantics.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Key layout I'd use:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:search:{portalId} ZSET (sliding 1s)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:meta:{portalId} HASH (last seen headers)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The two short-window buckets are sliding-window-log (Redis ZSET, score =","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"microtime","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The daily bucket is a plain","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"INCR","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with TTL set to seconds-until-midnight in","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"HubSpot's account timezone","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to read it","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to read it","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Two-phase: optimistic pre-check, authoritative post-update.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Before a call, run the Lua acquire script. If it returns \"denied,\" sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"After the call, parse","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Max","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Interval-Milliseconds","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and store them in","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:meta:{portalId}","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"For search specifically, since headers don't come back, the local counter","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"is","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"not","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"on 429 (you really did make that request).","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to work with multiple jobs","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to work with multiple jobs","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Concurrency cap","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"via","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Queue::throttle()","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Per-tenant fairness","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Priority lanes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot-priority","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot-bulk","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Backoff on 429","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— respect the","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry-After","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"header. If absent, exponential with jitter (e.g.,","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"min(2^attempt * 250ms, 30s) ± 20%","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to work with paginated requests","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to work with paginated requests","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The trap is treating \"fetch all\" as one logical operation. Each page is its own API call and competes for tokens with everything else.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Two practical rules:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Don't hold the worker idle while paginating.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"processing","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"of page N as a separate job, and queue a \"fetch page N+1\" job with a delay equal to the wait time. Each page becomes its own atomic unit.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Always batch where HubSpot offers it.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/{type}/batch/{read|update|create}","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"accepts up to 100 IDs per call. For your \"patching IDs\" flow this is the difference between 600 calls and 6.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Walkthrough: 600 opportunities","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Walkthrough: 600 opportunities","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Sane flow:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Identify deltas","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— if you can avoid Search at all, do. Use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Batch read","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"current state —","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/deals/batch/read","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with 100 IDs per call → 6 calls. Burst weight 6.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Compute diff","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"in your code (no API).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Batch update","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"—","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/deals/batch/update","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with 100 per call → 6 calls.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Associations","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"—","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v4/associations/{from}/{to}/batch/create","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"if needed → another few batch calls.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Recognition: pre-check denies if","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"<weight>","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"slots aren't free; on 429 the response surfaces","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"policyName","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(DAILY/SECONDLY) — log it, push the job back with","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry-After","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"delay, increment a metric so you can alarm on sustained 429 rate.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Pitfalls & bottlenecks worth pre-empting","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Pitfalls & bottlenecks worth pre-empting","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Daily quota is shared across the whole portal","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search is uniquely fragile","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":": separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hs_object_id","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"ranges or by","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"windows.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry storms after HubSpot incidents","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Webhook → sync → webhook loops","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"jy_last_sync_id","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":") and ignore webhooks where it just changed.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Token refresh","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"expires_in - 60s","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", single-flight the refresh (only one worker refreshes; others wait on a lock).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search latency for fresh writes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Clock skew","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— Redis is authoritative. Don't use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"time()","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"from each worker for window scoring; use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"redis.call('TIME')","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"inside the Lua script.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Dead workers with held tokens","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The 5% error rule","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Phantom contention with Laravel","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"RateLimited","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"middleware","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":": it's per-job-class by default, not per-portal. If your","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"SyncDealJob","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"UpdateActivityJob","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"HubspotRateLimiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"service that everything routes through.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give positive feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give negative feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"You said: What will happen if there are two different types of rate limits?","depth":20,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"You said: What will happen if there are two different types of rate limits?","depth":21,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"What will happen if there are two different types of rate limits?","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search requests that have 5 requests per second","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"13:40","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Edit","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"Claude responded: They're independent buckets — with one shared bucket on top.","depth":21,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Claude responded: They're independent buckets — with one shared bucket on top.","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Analyzed independent rate limit buckets and their practical interactions","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Analyzed independent rate limit buckets and their practical interactions","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"They're independent buckets — with one shared bucket on top.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"The three buckets and what each call hits","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"The three buckets and what each call hits","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"┌──────────────────┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search request ──→ │ Search: 5/sec │ ──┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"└──────────────────┘ │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"├──→ ┌─────────────────┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"┌──────────────────┐ │ │ Daily: 250k–1M │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"└──────────────────┘ └─────────────────┘","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"What this means practically","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"What this means practically","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting search secondly limit (5/s):","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Only further","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/search","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting burst limit (190/10s):","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting daily limit:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"So can one type of request block the other?","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"So can one type of request block the other?","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search → Burst:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"No.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Burst → Search:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"No.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Saturating burst leaves Search's 5/s untouched.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Either → Daily:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Yes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", but only by contributing to the same daily counter. It's volume that kills you here, not the type.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"In theory you can sustain ~50 search + ~190 other =","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"~240 calls per 10 seconds","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"indefinitely (until daily runs out), because the two buckets bill independently.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Implementation consequence","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Implementation consequence","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"php","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'search'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// for search endpoints only","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'burst'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// for everything else","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'daily'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// every call, both kinds","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('search')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"+","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('daily')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". A batch update calls","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('burst')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"+","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('daily')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":".","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"One subtle pitfall: when you backoff on a 429,","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"inspect","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"policyName","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"in the response body","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to know which bucket actually fired. If you see","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"SECONDLY","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The other subtlety: the burst response gives you","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.","depth":25,"on_screen":false,"role_description":"text"},{"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"}]...
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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
Retry
Edit
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Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
Copy to clipboard
php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.
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You said: Ok, so what will happen each time I make a request?
You said: Ok, so what will happen each time I make a request?
Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?
13:44
Retry
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Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Analyzed per-request overhead concerns and quantified Redis performance impact
Analyzed per-request overhead concerns and quantified Redis performance impact
Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out. But the overhead is much smaller than it sounds, and importantly, it's nowhere near your bottleneck.
What "two buckets per request" actually costs
What "two buckets per request" actually costs
Each request makes
one round trip to Redis
— not two — because you fold both bucket checks into a single Lua script that runs atomically server-side:
Copy to clipboard
lua
-- 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
[...
|
2427
|
NULL
|
NULL
|
NULL
|
|
2431
|
104
|
27
|
2026-05-07T11:17:09.874611+00:00
|
/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778152629874_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
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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
)
;...
|
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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"}]...
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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
)
;...
|
NULL
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NULL
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NULL
|
NULL
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2433
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104
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28
|
2026-05-07T11:17:11.064820+00:00
|
/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778152631064_m2.jpg...
|
Claude
|
Claude
|
True
|
NULL
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monitor_2
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NULL
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NULL
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NULL
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NULL
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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
Show more
10:42
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Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
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You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
Retry
Edit
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Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
Copy to clipboard
php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.
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You said: Ok, so what will happen each time I make a request?
You said: Ok, so what will happen each time I make a request?
Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?
13:44
Retry
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
(...
|
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That's the limit the 429 in your example is hitting (","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"secondly limit","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"on","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/crm/v3/objects/contact/search","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"). It's a separate bucket from the 190/10s, and critically, search responses do not include the","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-*","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"headers","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— you can't read remaining capacity off the response, you have to track it yourself.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Non-search endpoints return","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Max","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", and","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Interval-Milliseconds","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". The daily headers are not returned for OAuth-authenticated calls","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— that's enforced for marketplace listing, but it's also a sane operational target.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"So you have","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"four buckets","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Where to store it","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Where to store it","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"INCR","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"EXPIRE","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"plus Lua scripting for true CAS semantics.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Key layout I'd use:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:search:{portalId} ZSET (sliding 1s)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:meta:{portalId} HASH (last seen headers)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The two short-window buckets are sliding-window-log (Redis ZSET, score =","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"microtime","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The daily bucket is a plain","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"INCR","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with TTL set to seconds-until-midnight in","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"HubSpot's account timezone","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to read it","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to read it","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Two-phase: optimistic pre-check, authoritative post-update.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Before a call, run the Lua acquire script. If it returns \"denied,\" sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"After the call, parse","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Max","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Interval-Milliseconds","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and store them in","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:meta:{portalId}","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"For search specifically, since headers don't come back, the local counter","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"is","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"not","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"on 429 (you really did make that request).","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to work with multiple jobs","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to work with multiple jobs","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Concurrency cap","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"via","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Queue::throttle()","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Per-tenant fairness","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Priority lanes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot-priority","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot-bulk","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Backoff on 429","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— respect the","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry-After","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"header. If absent, exponential with jitter (e.g.,","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"min(2^attempt * 250ms, 30s) ± 20%","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to work with paginated requests","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to work with paginated requests","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The trap is treating \"fetch all\" as one logical operation. Each page is its own API call and competes for tokens with everything else.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Two practical rules:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Don't hold the worker idle while paginating.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"processing","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"of page N as a separate job, and queue a \"fetch page N+1\" job with a delay equal to the wait time. Each page becomes its own atomic unit.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Always batch where HubSpot offers it.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/{type}/batch/{read|update|create}","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"accepts up to 100 IDs per call. For your \"patching IDs\" flow this is the difference between 600 calls and 6.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Walkthrough: 600 opportunities","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Walkthrough: 600 opportunities","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Sane flow:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Identify deltas","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— if you can avoid Search at all, do. Use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Batch read","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"current state —","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/deals/batch/read","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with 100 IDs per call → 6 calls. Burst weight 6.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Compute diff","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"in your code (no API).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Batch update","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"—","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/deals/batch/update","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with 100 per call → 6 calls.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Associations","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"—","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v4/associations/{from}/{to}/batch/create","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"if needed → another few batch calls.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Recognition: pre-check denies if","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"<weight>","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"slots aren't free; on 429 the response surfaces","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"policyName","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(DAILY/SECONDLY) — log it, push the job back with","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry-After","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"delay, increment a metric so you can alarm on sustained 429 rate.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Pitfalls & bottlenecks worth pre-empting","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Pitfalls & bottlenecks worth pre-empting","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Daily quota is shared across the whole portal","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search is uniquely fragile","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":": separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hs_object_id","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"ranges or by","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"windows.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry storms after HubSpot incidents","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Webhook → sync → webhook loops","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"jy_last_sync_id","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":") and ignore webhooks where it just changed.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Token refresh","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"expires_in - 60s","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", single-flight the refresh (only one worker refreshes; others wait on a lock).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search latency for fresh writes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Clock skew","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— Redis is authoritative. Don't use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"time()","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"from each worker for window scoring; use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"redis.call('TIME')","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"inside the Lua script.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Dead workers with held tokens","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The 5% error rule","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Phantom contention with Laravel","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"RateLimited","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"middleware","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":": it's per-job-class by default, not per-portal. If your","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"SyncDealJob","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"UpdateActivityJob","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"HubspotRateLimiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"service that everything routes through.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give positive feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give negative feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"You said: What will happen if there are two different types of rate limits?","depth":20,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"You said: What will happen if there are two different types of rate limits?","depth":21,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"What will happen if there are two different types of rate limits?","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search requests that have 5 requests per second","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"13:40","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Edit","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"Claude responded: They're independent buckets — with one shared bucket on top.","depth":21,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Claude responded: They're independent buckets — with one shared bucket on top.","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Analyzed independent rate limit buckets and their practical interactions","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Analyzed independent rate limit buckets and their practical interactions","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"They're independent buckets — with one shared bucket on top.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"The three buckets and what each call hits","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"The three buckets and what each call hits","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"┌──────────────────┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search request ──→ │ Search: 5/sec │ ──┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"└──────────────────┘ │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"├──→ ┌─────────────────┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"┌──────────────────┐ │ │ Daily: 250k–1M │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"└──────────────────┘ └─────────────────┘","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"What this means practically","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"What this means practically","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting search secondly limit (5/s):","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Only further","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/search","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting burst limit (190/10s):","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting daily limit:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"So can one type of request block the other?","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"So can one type of request block the other?","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search → Burst:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"No.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Burst → Search:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"No.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Saturating burst leaves Search's 5/s untouched.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Either → Daily:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Yes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", but only by contributing to the same daily counter. It's volume that kills you here, not the type.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"In theory you can sustain ~50 search + ~190 other =","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"~240 calls per 10 seconds","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"indefinitely (until daily runs out), because the two buckets bill independently.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Implementation consequence","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Implementation consequence","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"php","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'search'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// for search endpoints only","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'burst'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// for everything else","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'daily'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// every call, both kinds","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('search')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"+","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('daily')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". A batch update calls","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('burst')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"+","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('daily')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":".","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"One subtle pitfall: when you backoff on a 429,","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"inspect","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"policyName","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"in the response body","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to know which bucket actually fired. If you see","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"SECONDLY","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The other subtlety: the burst response gives you","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.","depth":25,"on_screen":false,"role_description":"text"},{"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"}]...
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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
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:
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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:
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
(...
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/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778152661525_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.
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Give positive feedback
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Retry
You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
Retry
Edit
Copy
Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
Copy to clipboard
php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.
Copy
Give positive feedback
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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sidebar","depth":21,"bounds":{"left":0.47340426,"top":0.026336791,"width":0.010638298,"height":0.025538707},"on_screen":true,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Share chat","depth":21,"bounds":{"left":0.48537233,"top":0.026336791,"width":0.010638298,"height":0.025538707},"on_screen":true,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Claude finished the response","depth":21,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"You said: So just a solution for rate limit implementation.","depth":20,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"You said: So just a solution for rate limit implementation.","depth":21,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"updating activity (engagement, meeting, call, note)","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"searching for a company or contact","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"updating deal","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"patching IDs\nReview what the limit is at the moment in the HubSpot documentation and then think of the solution:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"where to store it","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"how to read it","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"how to work with multiple jobs","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines","depth":25,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"). Example of such a error Client error:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST https://api.hubapi.com/crm/v3/objects/contact/search","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"resulted in a","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"429 Too Many Requests","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"response: {\"status\":\"error\",\"message\":\"You have reached your secondly limit.\",\"errorType\":\"RATE_LIMIT\",\"correlationId\":\"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Show more","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"10:42","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"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"}]...
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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...
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2026-05-07T11:17:59.208936+00:00
|
/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778152679208_m2.jpg...
|
Claude
|
Claude
|
True
|
NULL
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monitor_2
|
NULL
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NULL
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NULL
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NULL
|
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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...
|
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Get this once and cache it.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to read it","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to read it","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Two-phase: optimistic pre-check, authoritative post-update.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Before a call, run the Lua acquire script. If it returns \"denied,\" sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"After the call, parse","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Max","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Interval-Milliseconds","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and store them in","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:meta:{portalId}","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"For search specifically, since headers don't come back, the local counter","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"is","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"not","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"on 429 (you really did make that request).","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to work with multiple jobs","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to work with multiple jobs","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Concurrency cap","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"via","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Queue::throttle()","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Per-tenant fairness","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Priority lanes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot-priority","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot-bulk","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Backoff on 429","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— respect the","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry-After","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"header. 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"},{"role":"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"}]...
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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.
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...
|
2435
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|
2439
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104
|
31
|
2026-05-07T11:18:12.921573+00:00
|
/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778152692921_m2.jpg...
|
Firefox
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Jiminny — Work
|
True
|
app.dev.jiminny.com/login
|
monitor_2
|
NULL
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NULL
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NULL
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Platform Sprint 3 Q2 - Platform Team - Scrum Board Platform Sprint 3 Q2 - Platform Team - Scrum Board - Jira
Platform Sprint 3 Q2 - Platform Team - Scrum Board - Jira
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
Service-Desk - Queues - Platform team - Service space - Jira
Service-Desk - Queues - Platform team - Service space - Jira
Jy 20807 check various issues with stages by nikolaybiaivanov · Pull Request #12041 · jiminny/app
Jy 20807 check various issues with stages by nikolaybiaivanov · Pull Request #12041 · jiminny/app
Sentry
Sentry
Pull requests · jiminny/app
Pull requests · jiminny/app
Userpilot | Ask Jiminny Report Generated
Userpilot | Ask Jiminny Report Generated
JY-20773 fix user pilot tracking ofr automated report generated by LakyLak · Pull Request #12024 · jiminny/app
JY-20773 fix user pilot tracking ofr automated report generated by LakyLak · Pull Request #12024 · jiminny/app
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Platform Sprint 3 Q2 - Platform Team - Scrum Board Platform Sprint 3 Q2 - Platform Team - Scrum Board - Jira
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SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
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JY-20773 fix user pilot tracking ofr automated report generated by LakyLak · Pull Request #12024 · jiminny/app
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SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
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Jy 20807 check various issues with stages by nikolaybiaivanov · Pull Request #12041 · jiminny/app
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JY-20773 fix user pilot tracking ofr automated report generated by LakyLak · Pull Request #12024 · jiminny/app
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JY-20543 add AJ reports User pilot tracking by LakyLak · Pull Request #11932 · jiminny/app
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Platform Sprint 3 Q2 - Platform Team - Scrum Board Platform Sprint 3 Q2 - Platform Team - Scrum Board - Jira
Platform Sprint 3 Q2 - Platform Team - Scrum Board - Jira
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
Service-Desk - Queues - Platform team - Service space - Jira
Service-Desk - Queues - Platform team - Service space - Jira
Jy 20807 check various issues with stages by nikolaybiaivanov · Pull Request #12041 · jiminny/app
Jy 20807 check various issues with stages by nikolaybiaivanov · Pull Request #12041 · jiminny/app
Sentry
Sentry
Pull requests · jiminny/app
Pull requests · jiminny/app
Userpilot | Ask Jiminny Report Generated
Userpilot | Ask Jiminny Report Generated
JY-20773 fix user pilot tracking ofr automated report generated by LakyLak · Pull Request #12024 · jiminny/app
JY-20773 fix user pilot tracking ofr automated report generated by LakyLak · Pull Request #12024 · jiminny/app
Jiminny
Jiminny
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Platform Sprint 3 Q2 - Platform Team - Scrum Board - Jira
Platform Sprint 3 Q2 - Platform Team - Scrum Board - Jira
Open context menu for Platform Sprint 3 Q2 - Platform Team - Scrum Board - Jira
Jiminny
Jiminny
Open context menu for Jiminny
Inbox (1,620) - [EMAIL] - Jiminny Mail
Inbox (1,620) - [EMAIL] - Jiminny Mail
Open context menu for Inbox (1,620) - [EMAIL] - Jiminny Mail
Pipelines - jiminny/app
Pipelines - jiminny/app
Open context menu for Pipelines - jiminny/app
Jiminny
Jiminny
Open context menu for Jiminny
Userpilot | Events
Userpilot | Events
Open context menu for Userpilot | Events
JY-20543 add AJ reports User pilot tracking by LakyLak · Pull Request #11932 · jiminny/app
JY-20543 add AJ reports User pilot tracking by LakyLak · Pull Request #11932 · jiminny/app
Open context menu for JY-20543 add AJ reports User pilot tracking by LakyLak · Pull Request #11932 · jiminny/app
Meet - Daily - Platform
Meet - Daily - Platform
Open context menu for Meet - Daily - Platform
Customize
Customize...
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2439
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NULL
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NULL
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NULL
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2441
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2026-05-07T11:18:17.694543+00:00
|
/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778152697694_m2.jpg...
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Raycast
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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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NULL
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Today
Image (1150x618)
https://api.hubapi.com/acco Today
Image (1150x618)
https://api.hubapi.com/account-info/v3/details
https://api.hubapi.com/account-info/v3/api-usage/daily/private-apps
https://api.hubapi.com/account-info/v3/detail
Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response:…...
|
[{"role":"AXStaticText","text& [{"role":"AXStaticText","text":"Today","depth":5,"bounds":{"left":0.6821808,"top":0.4820431,"width":0.013297873,"height":0.011971269},"on_screen":true,"role_description":"text"},{"role":"AXStaticText","text":"Image (1150x618)","depth":5,"bounds":{"left":0.6928192,"top":0.509178,"width":0.037732713,"height":0.014365523},"on_screen":true,"role_description":"text"},{"role":"AXStaticText","text":"https://api.hubapi.com/account-info/v3/details","depth":5,"bounds":{"left":0.6928192,"top":0.54110134,"width":0.07480053,"height":0.014365523},"on_screen":true,"lines":[{"char_start":0,"char_count":31,"bounds":{"left":0.69348407,"top":0.54110134,"width":0.06691073,"height":0.014365523}},{"char_start":31,"char_count":15,"bounds":{"left":0.69348407,"top":0.5554669,"width":0.029778501,"height":0.014365523}}],"role_description":"text"},{"role":"AXStaticText","text":"https://api.hubapi.com/account-info/v3/api-usage/daily/private-apps","depth":5,"bounds":{"left":0.6928192,"top":0.57302475,"width":0.07480053,"height":0.014365523},"on_screen":true,"lines":[{"char_start":0,"char_count":31,"bounds":{"left":0.69348407,"top":0.57302475,"width":0.06691073,"height":0.014365523}},{"char_start":31,"char_count":32,"bounds":{"left":0.69348407,"top":0.58739024,"width":0.066070326,"height":0.014365523}},{"char_start":63,"char_count":4,"bounds":{"left":0.69348407,"top":0.6017558,"width":0.010133492,"height":0.014365523}}],"role_description":"text"},{"role":"AXStaticText","text":"https://api.hubapi.com/account-info/v3/detail","depth":5,"bounds":{"left":0.6928192,"top":0.6049481,"width":0.07480053,"height":0.014365523},"on_screen":true,"lines":[{"char_start":0,"char_count":31,"bounds":{"left":0.69348407,"top":0.6049481,"width":0.06691073,"height":0.014365523}},{"char_start":31,"char_count":14,"bounds":{"left":0.69348407,"top":0.61931366,"width":0.027464068,"height":0.014365523}}],"role_description":"text"},{"role":"AXStaticText","text":"Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response:…","depth":5,"bounds":{"left":0.6928192,"top":0.6368715,"width":0.07480053,"height":0.014365523},"on_screen":true}]...
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7894064927038312960
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6597248997804524416
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visual_change
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accessibility
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Today
Image (1150x618)
https://api.hubapi.com/acco Today
Image (1150x618)
https://api.hubapi.com/account-info/v3/details
https://api.hubapi.com/account-info/v3/api-usage/daily/private-apps
https://api.hubapi.com/account-info/v3/detail
Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response:…...
|
NULL
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NULL
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NULL
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NULL
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2442
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2026-05-07T11:18:20.414817+00:00
|
/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778152700414_m2.jpg...
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Raycast
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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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NULL
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Today
Image (1150x618)
[URL_WITH_CREDENTIALS] ~/ji Today
Image (1150x618)
[URL_WITH_CREDENTIALS] ~/jiminny/app (JY-20157-AJ-report-not-send-notification) $ git fetch origin…
lukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-20372-ai-reports-promotion-pages) $ sp-start…
app/Services/Crm/Hubspot/Client
app/Services/Crm/Hubspot/Client.php:621
private function executeRequest(callable $apiCall)…
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines...
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[{"role":"AXStaticText","text& [{"role":"AXStaticText","text":"Today","depth":5,"bounds":{"left":0.6821808,"top":0.4820431,"width":0.013297873,"height":0.011971269},"on_screen":true,"role_description":"text"},{"role":"AXStaticText","text":"Image (1150x618)","depth":5,"bounds":{"left":0.6928192,"top":0.509178,"width":0.037732713,"height":0.014365523},"on_screen":true,"role_description":"text"},{"role":"AXStaticText","text":"https://api.hubapi.com/account-info/v3/details","depth":5,"bounds":{"left":0.6928192,"top":0.54110134,"width":0.07480053,"height":0.014365523},"on_screen":true,"lines":[{"char_start":0,"char_count":31,"bounds":{"left":0.69348407,"top":0.54110134,"width":0.06691073,"height":0.014365523}},{"char_start":31,"char_count":15,"bounds":{"left":0.69348407,"top":0.5554669,"width":0.029778501,"height":0.014365523}}],"role_description":"text"},{"role":"AXStaticText","text":"https://api.hubapi.com/account-info/v3/api-usage/daily/private-apps","depth":5,"bounds":{"left":0.6928192,"top":0.57302475,"width":0.07480053,"height":0.014365523},"on_screen":true,"lines":[{"char_start":0,"char_count":31,"bounds":{"left":0.69348407,"top":0.57302475,"width":0.06691073,"height":0.014365523}},{"char_start":31,"char_count":32,"bounds":{"left":0.69348407,"top":0.58739024,"width":0.066070326,"height":0.014365523}},{"char_start":63,"char_count":4,"bounds":{"left":0.69348407,"top":0.6017558,"width":0.010133492,"height":0.014365523}}],"role_description":"text"},{"role":"AXStaticText","text":"https://api.hubapi.com/account-info/v3/detail","depth":5,"bounds":{"left":0.6928192,"top":0.6049481,"width":0.07480053,"height":0.014365523},"on_screen":true,"lines":[{"char_start":0,"char_count":31,"bounds":{"left":0.69348407,"top":0.6049481,"width":0.06691073,"height":0.014365523}},{"char_start":31,"char_count":14,"bounds":{"left":0.69348407,"top":0.61931366,"width":0.027464068,"height":0.014365523}}],"role_description":"text"},{"role":"AXStaticText","text":"Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response:…","depth":5,"bounds":{"left":0.6928192,"top":0.6368715,"width":0.07480053,"height":0.014365523},"on_screen":true,"lines":[{"char_start":0,"char_count":28,"bounds":{"left":0.69348407,"top":0.6368715,"width":0.05513494,"height":0.014365523}},{"char_start":28,"char_count":30,"bounds":{"left":0.69348407,"top":0.651237,"width":0.06657572,"height":0.014365523}},{"char_start":58,"char_count":35,"bounds":{"left":0.69348407,"top":0.66560256,"width":0.07149003,"height":0.014365523}},{"char_start":93,"char_count":29,"bounds":{"left":0.69348407,"top":0.67996806,"width":0.06771156,"height":0.014365523}}],"role_description":"text"},{"role":"AXStaticText","text":"API usage guidelines and limits - HubSpot docs","depth":5,"bounds":{"left":0.6928192,"top":0.6687949,"width":0.07480053,"height":0.014365523},"on_screen":true,"lines":[{"char_start":0,"char_count":34,"bounds":{"left":0.69348407,"top":0.6687949,"width":0.06881896,"height":0.014365523}},{"char_start":34,"char_count":12,"bounds":{"left":0.69348407,"top":0.6831604,"width":0.029440308,"height":0.014365523}}],"role_description":"text"},{"role":"AXStaticText","text":"Image (1625x828)","depth":5,"bounds":{"left":0.6928192,"top":0.7007183,"width":0.03939495,"height":0.014365523},"on_screen":true,"role_description":"text"},{"role":"AXStaticText","text":"Yesterday","depth":5,"bounds":{"left":0.6821808,"top":0.73423785,"width":0.020777926,"height":0.011971269},"on_screen":true,"role_description":"text"},{"role":"AXStaticText","text":"https://github.com/screenpipe/screenpipe","depth":5,"bounds":{"left":0.6928192,"top":0.7613727,"width":0.07480053,"height":0.014365523},"on_screen":true,"lines":[{"char_start":0,"char_count":19,"bounds":{"left":0.69348407,"top":0.7613727,"width":0.03971363,"height":0.014365523}},{"char_start":19,"char_count":21,"bounds":{"left":0.69348407,"top":0.77573824,"width":0.047769554,"height":0.014365523}}],"role_description":"text"},{"role":"AXStaticText","text":"This Year","depth":5,"bounds":{"left":0.6821808,"top":0.79489225,"width":0.019614361,"height":0.011971269},"on_screen":true,"role_description":"text"},{"role":"AXStaticText","text":"Оправих flanky тестове който fail-ваха и двата PR-а могат да се деплойват https://github.com/jiminny/app/pull/12011 и https://github.com/jiminny/app/pull/12016","depth":5,"bounds":{"left":0.6928192,"top":0.82202715,"width":0.07480053,"height":0.014365523},"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"https://github.com/jiminny/app/pull/12016","depth":5,"bounds":{"left":0.6928192,"top":0.8539505,"width":0.07480053,"height":0.014365523},"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"https://github.com/jiminny/app/pull/12011","depth":5,"bounds":{"left":0.6928192,"top":0.8858739,"width":0.07480053,"height":0.014365523},"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-20157-AJ-report-not-send-notification) $ git fetch origin…","depth":5,"bounds":{"left":0.6928192,"top":0.91779727,"width":0.07480053,"height":0.014365523},"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-20372-ai-reports-promotion-pages) $ sp-start…","depth":5,"bounds":{"left":0.6928192,"top":0.9497207,"width":0.07480053,"height":0.014365523},"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"app/Services/Crm/Hubspot/Client","depth":5,"bounds":{"left":0.6928192,"top":0.98164403,"width":0.07180851,"height":0.014365523},"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"app/Services/Crm/Hubspot/Client.php:621","depth":5,"bounds":{"left":0.6928192,"top":1.0,"width":0.07480053,"height":-0.013567448},"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"private function executeRequest(callable $apiCall)…","depth":5,"bounds":{"left":0.6928192,"top":1.0,"width":0.07480053,"height":-0.04549086},"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines","depth":5,"bounds":{"left":0.6928192,"top":1.0,"width":0.07480053,"height":-0.077414155},"on_screen":false,"role_description":"text"}]...
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-7596123220321702372
|
8035833180152423491
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visual_change
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accessibility
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NULL
|
Today
Image (1150x618)
[URL_WITH_CREDENTIALS] ~/ji Today
Image (1150x618)
[URL_WITH_CREDENTIALS] ~/jiminny/app (JY-20157-AJ-report-not-send-notification) $ git fetch origin…
lukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-20372-ai-reports-promotion-pages) $ sp-start…
app/Services/Crm/Hubspot/Client
app/Services/Crm/Hubspot/Client.php:621
private function executeRequest(callable $apiCall)…
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines...
|
2441
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NULL
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NULL
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NULL
|
|
2443
|
104
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35
|
2026-05-07T11:18:29.507146+00:00
|
/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778152709507_m2.jpg...
|
Firefox
|
Work — Mozilla Firefox
|
True
|
NULL
|
monitor_2
|
NULL
|
NULL
|
NULL
|
NULL
|
Platform Sprint 3 Q2 - Platform Team - Scrum Board Platform Sprint 3 Q2 - Platform Team - Scrum Board - Jira
Platform Sprint 3 Q2 - Platform Team - Scrum Board - Jira
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
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Jy 20807 check various issues with stages by nikolaybiaivanov · Pull Request #12041 · jiminny/app
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JY-20773 fix user pilot tracking ofr automated report generated by LakyLak · Pull Request #12024 · jiminny/app
JY-20773 fix user pilot tracking ofr automated report generated by LakyLak · Pull Request #12024 · jiminny/app
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JY-20543 add AJ reports User pilot tracking by LakyLak · Pull Request #11932 · jiminny/app
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Platform Sprint 3 Q2 - Platform Team - Scrum Board Platform Sprint 3 Q2 - Platform Team - Scrum Board - Jira
Platform Sprint 3 Q2 - Platform Team - Scrum Board - Jira
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
Service-Desk - Queues - Platform team - Service space - Jira
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Jy 20807 check various issues with stages by nikolaybiaivanov · Pull Request #12041 · jiminny/app
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JY-20773 fix user pilot tracking ofr automated report generated by LakyLak · Pull Request #12024 · jiminny/app
JY-20773 fix user pilot tracking ofr automated report generated by LakyLak · Pull Request #12024 · jiminny/app
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JY-20543 add AJ reports User pilot tracking by LakyLak · Pull Request #11932 · jiminny/app
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Platform Sprint 3 Q2 - Platform Team - Scrum Board Platform Sprint 3 Q2 - Platform Team - Scrum Board - Jira
Platform Sprint 3 Q2 - Platform Team - Scrum Board - Jira
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
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JY-20773 fix user pilot tracking ofr automated report generated by LakyLak · Pull Request #12024 · jiminny/app
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JY-20543 add AJ reports User pilot tracking by LakyLak · Pull Request #11932 · jiminny/app
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Platform Sprint 3 Q2 - Platform Team - Scrum Board Platform Sprint 3 Q2 - Platform Team - Scrum Board - Jira
Platform Sprint 3 Q2 - Platform Team - Scrum Board - Jira
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
Service-Desk - Queues - Platform team - Service space - Jira
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Jy 20807 check various issues with stages by nikolaybiaivanov · Pull Request #12041 · jiminny/app
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Userpilot | Ask Jiminny Report Generated
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JY-20773 fix user pilot tracking ofr automated report generated by LakyLak · Pull Request #12024 · jiminny/app
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Platform Sprint 3 Q2 - Platform Team - Scrum Board Platform Sprint 3 Q2 - Platform Team - Scrum Board - Jira
Platform Sprint 3 Q2 - Platform Team - Scrum Board - Jira
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
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Service-Desk - Queues - Platform team - Service space - Jira
Jy 20807 check various issues with stages by nikolaybiaivanov · Pull Request #12041 · jiminny/app
Jy 20807 check various issues with stages by nikolaybiaivanov · Pull Request #12041 · jiminny/app
Sentry
Sentry
Pull requests · jiminny/app
Pull requests · jiminny/app
Userpilot | Ask Jiminny Report Generated
Userpilot | Ask Jiminny Report Generated
JY-20773 fix user pilot tracking ofr automated report generated by LakyLak · Pull Request #12024 · jiminny/app
JY-20773 fix user pilot tracking ofr automated report generated by LakyLak · Pull Request #12024 · jiminny/app
Jiminny
Jiminny
API usage guidelines and limits - HubSpot docs
API usage guidelines and limits - HubSpot docs
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Authentication and security
Authentication and security
Checking API usage
Checking API usage
Apps built on version 2025.2 and 2026.03 of the developer platform
Apps built on version 2025.2 and 2026.03 of the developer platform
Legacy apps
Legacy apps
Rate limits
Rate limits
Publicly distributed OAuth app limits
Publicly distributed OAuth app limits
Privately distributed app limits
Privately distributed app limits
Exemptions
Exemptions
App and account limits
App and account limits
API-specific limits
API-specific limits
Timeline event limits
Timeline event limits
Custom event limits
Custom event limits
Service limits
Service limits
Rate limit error responses
Rate limit error responses
Address rate limit issues
Address rate limit issues
Use batch APIs and cache results when possible
Use batch APIs and cache results when possible
Use webhooks to get updated data from HubSpot
Use webhooks to get updated data from HubSpot
Platform
API usage guidelines and limits
API usage guidelines and limits
Learn about the different API and developer feature limits in HubSpot.
Documentation Index
Documentation Index
Fetch the complete documentation index at:
https://developers.hubspot.com/docs/llms.txt
https://developers.hubspot.com/docs/llms.txt
Use this file to discover all available pages before exploring further.
HubSpot closely monitors usage of all APIs to ensure a quality experience for every user. All app and integration developers must comply with the
HubSpot Acceptable Use Policy
HubSpot Acceptable Use Policy
and
API Terms
API Terms
. While HubSpot reserves the right to change or deprecate the APIs over time, updates will always be provided in advance through the
Developer Changelog
Developer Changelog
.
Navigate to header Authentication and security
Navigate to header
Authentication and security
For optimal security, all apps must use HubSpot’s
OAuth protocol
OAuth protocol
directly, or use your app’s static auth token if you’re building an
app installed in a single account
app installed in a single account
. Apps are responsible for storing time-to-live (TTL) data and refreshing user access tokens in accordance with this protocol. When an access token is generated, it will include an
expires_in
parameter indicating how long it can be used to make API calls before refreshing.
Unauthorized (401)
requests are not a valid indicator that a new access token must be retrieved.
Navigate to header Checking API usage
Navigate to header
Checking API usage
Navigate to header Apps built on version 2025.2 and 2026.03 of the developer platform
Navigate to header
Apps built on version 2025.2 and 2026.03 of the developer platform
To view API usage for your apps built on the
new developer platform
new developer platform
:
In your HubSpot account, navigate to
Development
in the main navigation bar.
In the left sidebar menu, navigate to
Monitoring
>
API call usage
.
Your API usage across all your apps will be listed at the top of the page. You can also review usage for the apps you built in your specific account, along with any third-party apps you’ve installed.
Navigate to header Legacy apps
Navigate to header
Legacy apps
To view API usage for a legacy
private app
private app
:
In your HubSpot account, navigate to
Development
in the main navigation bar.
In the left sidebar menu, navigate to
Legacy apps
.
Click the
name
of the private app.
On the app details page, click the
Logs
tab.
Review the API calls listed in the table. You can also use the
search bar
,
filters
, and
date pickers
to further refine the displayed API calls.
Learn more about
checking API usage in legacy private apps
checking API usage in legacy private apps
.
To view API usage for a public app using OAuth:
In your HubSpot account, navigate to
Development
in the main navigation bar.
In the left sidebar menu, navigate to
Monitoring
, then select
Logs
.
At the top, select the
name
of the app.
Use the
tabs
to view different types of requests being made to or from the app. While viewing these logs, you can click an
individual request
to view more information.
Learn more about
monitoring API usage for legacy public apps
monitoring API usage for legacy public apps
.
Navigate to header Rate limits
Navigate to header
Rate limits
The number of calls your app can make is based on your account subscription in the account it’s installed in, and whether you’ve purchased the
API limit increase
API limit increase
.
You can keep the following general limits in mind when making requests to HubSpot’s API endpoints, however some APIs may also have more specific limits listed on their relevant API guide (e.g., the
exports API
exports API
).
The following APIs have limits that are unique from or stricter than the general limits. Refer to each associated guide for more information.
Associations API
Associations API
CRM Search API
CRM Search API
GraphQL API
GraphQL API
Navigate to header Publicly distributed OAuth app limits
Navigate to header
Publicly distributed OAuth app limits
For
legacy public apps
legacy public apps
, and apps on the latest versions of the developer platform (2025.2 and 2026.03) using
OAuth authentication
OAuth authentication
distributed
distributed
via the HubSpot marketplace, each HubSpot account that installs your app is limited to 110 requests every 10 seconds. This excludes the
CRM Search API
CRM Search API
.
The
API limit increase add-on
API limit increase add-on
does
not
increase the limits of
legacy public apps
legacy public apps
nor apps on the latest versions of the developer platform (2025.2 and 2026.03) using
OAuth authentication
OAuth authentication
that are
distributed
distributed
via the HubSpot marketplace.
Navigate to header Privately distributed app limits
Navigate to header
Privately distributed app limits
The limits in the table below apply to
legacy private apps
legacy private apps
, as well as apps on the latest versions of the developer platform (2025.2 and 2026.03) with a
distribution
distribution
set to
private
. Note that for these privately distributed app types:
The burst limit, detailed in the
Per 10 seconds
column below, applies individually per app.
The daily limit, detailed in the
Per day
column below, is shared across all apps within the same HubSpot account.
Product Tier
Per 10 Seconds
Per Day
Privately distributed apps
(Any Hub)
Free and Starter
100 / app
250,000 / account
(Any Hub)
Professional
190 / app
625,000 / account
(Any Hub)...
|
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more specific limits listed on their relevant API guide (e.g., the","depth":10,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"exports API","depth":10,"on_screen":false,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"exports API","depth":11,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":").","depth":10,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"The following APIs have limits that are unique from or stricter than the general limits. Refer to each associated guide for more information.","depth":11,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"Associations API","depth":12,"on_screen":false,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"Associations API","depth":13,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"CRM Search API","depth":12,"on_screen":false,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"CRM Search API","depth":13,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"GraphQL API","depth":12,"on_screen":false,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"GraphQL API","depth":13,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXHeading","text":"Navigate to header Publicly distributed OAuth app limits","depth":9,"on_screen":false,"help_text":"","role_description":"heading","subrole":"AXUnknown"},{"role":"AXLink","text":"Navigate to header","depth":11,"on_screen":false,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Publicly distributed OAuth app limits","depth":11,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"For","depth":10,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"legacy public apps","depth":10,"on_screen":false,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"legacy public apps","depth":11,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":", and apps on the latest versions of the developer platform (2025.2 and 2026.03) using","depth":10,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"OAuth authentication","depth":10,"on_screen":false,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"OAuth authentication","depth":11,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"distributed","depth":10,"on_screen":false,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"distributed","depth":11,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"via the HubSpot marketplace, each HubSpot account that installs your app is limited to 110 requests every 10 seconds. This excludes the","depth":10,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"CRM Search API","depth":10,"on_screen":false,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"CRM Search API","depth":11,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":".","depth":10,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"The","depth":11,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"API limit increase add-on","depth":11,"on_screen":false,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"API limit increase add-on","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"does","depth":11,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"not","depth":11,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"increase the limits of","depth":11,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"legacy public apps","depth":11,"on_screen":false,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"legacy public apps","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"nor apps on the latest versions of the developer platform (2025.2 and 2026.03) using","depth":11,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"OAuth authentication","depth":11,"on_screen":false,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"OAuth authentication","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"that are","depth":11,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"distributed","depth":11,"on_screen":false,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"distributed","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"via the HubSpot marketplace.","depth":11,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXHeading","text":"Navigate to header Privately distributed app limits","depth":9,"on_screen":false,"help_text":"","role_description":"heading","subrole":"AXUnknown"},{"role":"AXLink","text":"Navigate to header","depth":11,"on_screen":false,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Privately distributed app limits","depth":11,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"The limits in the table below apply to","depth":10,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"legacy private apps","depth":10,"on_screen":false,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"legacy private apps","depth":11,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":", as well as apps on the latest versions of the developer platform (2025.2 and 2026.03) with a","depth":10,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"distribution","depth":10,"on_screen":false,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"distribution","depth":11,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"set to","depth":10,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"private","depth":11,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":". Note that for these privately distributed app types:","depth":10,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"The burst limit, detailed in the","depth":11,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Per 10 seconds","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"column below, applies individually per app.","depth":11,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"The daily limit, detailed in the","depth":11,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Per day","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"column below, is shared across all apps within the same HubSpot account.","depth":11,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Product Tier","depth":15,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Per 10 Seconds","depth":15,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Per Day","depth":15,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Privately distributed apps","depth":14,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"(Any Hub)","depth":14,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Free and Starter","depth":14,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"100 / app","depth":14,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"250,000 / account","depth":14,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"(Any Hub)","depth":14,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Professional","depth":14,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"190 / app","depth":14,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"625,000 / account","depth":14,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"(Any Hub)","depth":14,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"}]...
|
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Platform Sprint 3 Q2 - Platform Team - Scrum Board Platform Sprint 3 Q2 - Platform Team - Scrum Board - Jira
Platform Sprint 3 Q2 - Platform Team - Scrum Board - Jira
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
Service-Desk - Queues - Platform team - Service space - Jira
Service-Desk - Queues - Platform team - Service space - Jira
Jy 20807 check various issues with stages by nikolaybiaivanov · Pull Request #12041 · jiminny/app
Jy 20807 check various issues with stages by nikolaybiaivanov · Pull Request #12041 · jiminny/app
Sentry
Sentry
Pull requests · jiminny/app
Pull requests · jiminny/app
Userpilot | Ask Jiminny Report Generated
Userpilot | Ask Jiminny Report Generated
JY-20773 fix user pilot tracking ofr automated report generated by LakyLak · Pull Request #12024 · jiminny/app
JY-20773 fix user pilot tracking ofr automated report generated by LakyLak · Pull Request #12024 · jiminny/app
Jiminny
Jiminny
API usage guidelines and limits - HubSpot docs
API usage guidelines and limits - HubSpot docs
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Authentication and security
Authentication and security
Checking API usage
Checking API usage
Apps built on version 2025.2 and 2026.03 of the developer platform
Apps built on version 2025.2 and 2026.03 of the developer platform
Legacy apps
Legacy apps
Rate limits
Rate limits
Publicly distributed OAuth app limits
Publicly distributed OAuth app limits
Privately distributed app limits
Privately distributed app limits
Exemptions
Exemptions
App and account limits
App and account limits
API-specific limits
API-specific limits
Timeline event limits
Timeline event limits
Custom event limits
Custom event limits
Service limits
Service limits
Rate limit error responses
Rate limit error responses
Address rate limit issues
Address rate limit issues
Use batch APIs and cache results when possible
Use batch APIs and cache results when possible
Use webhooks to get updated data from HubSpot
Use webhooks to get updated data from HubSpot
Platform
API usage guidelines and limits
API usage guidelines and limits
Learn about the different API and developer feature limits in HubSpot.
Documentation Index
Documentation Index
Fetch the complete documentation index at:
https://developers.hubspot.com/docs/llms.txt
https://developers.hubspot.com/docs/llms.txt
Use this file to discover all available pages before exploring further.
HubSpot closely monitors usage of all APIs to ensure a quality experience for every user. All app and integration developers must comply with the
HubSpot Acceptable Use Policy
HubSpot Acceptable Use Policy
and
API Terms
API Terms
. While HubSpot reserves the right to change or deprecate the APIs over time, updates will always be provided in advance through the
Developer Changelog
Developer Changelog
.
Navigate to header Authentication and security
Navigate to header
Authentication and security
For optimal security, all apps must use HubSpot’s
OAuth protocol
OAuth protocol
directly, or use your app’s static auth token if you’re building an
app installed in a single account
app installed in a single account
. Apps are responsible for storing time-to-live (TTL) data and refreshing user access tokens in accordance with this protocol. When an access token is generated, it will include an
expires_in
parameter indicating how long it can be used to make API calls before refreshing.
Unauthorized (401)
requests are not a valid indicator that a new access token must be retrieved.
Navigate to header Checking API usage
Navigate to header
Checking API usage
Navigate to header Apps built on version 2025.2 and 2026.03 of the developer platform
Navigate to header
Apps built on version 2025.2 and 2026.03 of the developer platform
To view API usage for your apps built on the
new developer platform
new developer platform
:
In your HubSpot account, navigate to
Development
in the main navigation bar.
In the left sidebar menu, navigate to
Monitoring
>
API call usage
.
Your API usage across all your apps will be listed at the top of the page. You can also review usage for the apps you built in your specific account, along with any third-party apps you’ve installed.
Navigate to header Legacy apps
Navigate to header
Legacy apps
To view API usage for a legacy
private app
private app
:
In your HubSpot account, navigate to
Development
in the main navigation bar.
In the left sidebar menu, navigate to
Legacy apps
.
Click the
name
of the private app.
On the app details page, click the
Logs
tab.
Review the API calls listed in the table. You can also use the
search bar
,
filters
, and
date pickers
to further refine the displayed API calls.
Learn more about
checking API usage in legacy private apps
checking API usage in legacy private apps
.
To view API usage for a public app using OAuth:
In your HubSpot account, navigate to
Development
in the main navigation bar.
In the left sidebar menu, navigate to
Monitoring
, then select
Logs
.
At the top, select the
name
of the app.
Use the
tabs
to view different types of requests being made to or from the app. While viewing these logs, you can click an
individual request
to view more information.
Learn more about
monitoring API usage for legacy public apps
monitoring API usage for legacy public apps
.
Navigate to header Rate limits
Navigate to header
Rate limits
The number of calls your app can make is based on your account subscription in the account it’s installed in, and whether you’ve purchased the
API limit increase
API limit increase
.
You can keep the following general limits in mind when making requests to HubSpot’s API endpoints, however some APIs may also have more specific limits listed on their relevant API guide (e.g., the
exports API
exports API
).
The following APIs have limits that are unique from or stricter than the general limits. Refer to each associated guide for more information.
Associations API
Associations API
CRM Search API
CRM Search API
GraphQL API
GraphQL API
Navigate to header Publicly distributed OAuth app limits
Navigate to header
Publicly distributed OAuth app limits
For
legacy public apps
legacy public apps
, and apps on the latest versions of the developer platform (2025.2 and 2026.03) using
OAuth authentication
OAuth authentication
distributed
distributed
via the HubSpot marketplace, each HubSpot account that installs your app is limited to 110 requests every 10 seconds. This excludes the
CRM Search API
CRM Search API
.
The
API limit increase add-on
API limit increase add-on
does
not
increase the limits of
legacy public apps
legacy public apps
nor apps on the latest versions of the developer platform (2025.2 and 2026.03) using
OAuth authentication
OAuth authentication
that are
distributed
distributed
via the HubSpot marketplace.
Navigate to header Privately distributed app limits
Navigate to header
Privately distributed app limits
The limits in the table below apply to
legacy private apps
legacy private apps
, as well as apps on the latest versions of the developer platform (2025.2 and 2026.03) with a
distribution
distribution
set to
private
. Note that for these privately distributed app types:
The burst limit, detailed in the
Per 10 seconds
column below, applies individually per app.
The daily limit, detailed in the
Per day
column below, is shared across all apps within the same HubSpot account.
Product Tier
Per 10 Seconds
Per Day
Privately distributed apps
(Any Hub)
Free and Starter
100 / app
250,000 / account
(Any Hub)
Professional
190 / app
625,000 / account
(Any Hub)...
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API usage guidelines and limits - HubSpot docs — W API usage guidelines and limits - HubSpot docs — Work...
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Platform Sprint 3 Q2 - Platform Team - Scrum Board Platform Sprint 3 Q2 - Platform Team - Scrum Board - Jira
Platform Sprint 3 Q2 - Platform Team - Scrum Board - Jira
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
Service-Desk - Queues - Platform team - Service space - Jira
Service-Desk - Queues - Platform team - Service space - Jira
Jy 20807 check various issues with stages by nikolaybiaivanov · Pull Request #12041 · jiminny/app
Jy 20807 check various issues with stages by nikolaybiaivanov · Pull Request #12041 · jiminny/app
Sentry
Sentry
Pull requests · jiminny/app
Pull requests · jiminny/app
Userpilot | Ask Jiminny Report Generated
Userpilot | Ask Jiminny Report Generated
JY-20773 fix user pilot tracking ofr automated report generated by LakyLak · Pull Request #12024 · jiminny/app
JY-20773 fix user pilot tracking ofr automated report generated by LakyLak · Pull Request #12024 · jiminny/app
Jiminny
Jiminny
API usage guidelines and limits - HubSpot docs
API usage guidelines and limits - HubSpot docs
Close tab
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Overview
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Platform
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API usage guidelines
API usage guidelines
APIs by tier
APIs by tier
Breaking change guidelines
Breaking change guidelines
Local development
Local development
Toggle HubSpot CLI section
HubSpot CLI
Build with config profiles
Build with config profiles
Configurable test accounts
Configurable test accounts
Toggle Developer MCP section
Developer MCP
VS Code extension
VS Code extension
Troubleshooting
Troubleshooting
Third-party tools
Third-party tools
Set up GitHub Actions
Set up GitHub Actions
Postman for HubSpot APIs
Postman for HubSpot APIs
SDKs
SDKs
Node
Node
Php
Php
Python
Python
Ruby
Ruby
English
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Toggle dark mode
close
close
On this page
On this page
Authentication and security
Authentication and security
Checking API usage
Checking API usage
Apps built on version 2025.2 and 2026.03 of the developer platform
Apps built on version 2025.2 and 2026.03 of the developer platform
Legacy apps
Legacy apps
Rate limits
Rate limits
Publicly distributed OAuth app limits
Publicly distributed OAuth app limits
Privately distributed app limits
Privately distributed app limits
Exemptions
Exemptions
App and account limits
App and account limits
API-specific limits
API-specific limits
Timeline event limits
Timeline event limits
Custom event limits
Custom event limits
Service limits
Service limits
Rate limit error responses
Rate limit error responses
Address rate limit issues
Address rate limit issues
Use batch APIs and cache results when possible
Use batch APIs and cache results when possible
Use webhooks to get updated data from HubSpot
Use webhooks to get updated data from HubSpot
Platform
API usage guidelines and limits
API usage guidelines and limits
Learn about the different API and developer feature limits in HubSpot.
Documentation Index
Documentation Index
Fetch the complete documentation index at:
https://developers.hubspot.com/docs/llms.txt
https://developers.hubspot.com/docs/llms.txt
Use this file to discover all available pages before exploring further.
HubSpot closely monitors usage of all APIs to ensure a quality experience for every user. All app and integration developers must comply with the
HubSpot Acceptable Use Policy
HubSpot Acceptable Use Policy
and
API Terms
API Terms
. While HubSpot reserves the right to change or deprecate the APIs over time, updates will always be provided in advance through the
Developer Changelog
Developer Changelog
.
Navigate to header Authentication and security
Navigate to header
Authentication and security
For optimal security, all apps must use HubSpot’s
OAuth protocol
OAuth protocol
directly, or use your app’s static auth token if you’re building an
app installed in a single account
app installed in a single account
. Apps are responsible for storing time-to-live (TTL) data and refreshing user access tokens in accordance with this protocol. When an access token is generated, it will include an
expires_in
parameter indicating how long it can be used to make API calls before refreshing.
Unauthorized (401)
requests are not a valid indicator that a new access token must be retrieved.
Navigate to header Checking API usage
Navigate to header
Checking API usage
Navigate to header Apps built on version 2025.2 and 2026.03 of the developer platform
Navigate to header
Apps built on version 2025.2 and 2026.03 of the developer platform
To view API usage for your apps built on the
new developer platform
new developer platform
:
In your HubSpot account, navigate to
Development
in the main navigation bar.
In the left sidebar menu, navigate to
Monitoring
>
API call usage
.
Your API usage across all your apps will be listed at the top of the page. You can also review usage for the apps you built in your specific account, along with any third-party apps you’ve installed.
Navigate to header Legacy apps
Navigate to header
Legacy apps
To view API usage for a legacy
private app
private app
:
In your HubSpot account, navigate to
Development
in the main navigation bar.
In the left sidebar menu, navigate to
Legacy apps
.
Click the
name
of the private app.
On the app details page, click the
Logs
tab.
Review the API calls listed in the table. You can also use the
search bar
,
filters
, and
date pickers
to further refine the displayed API calls.
Learn more about
checking API usage in legacy private apps
checking API usage in legacy private apps
.
To view API usage for a public app using OAuth:
In your HubSpot account, navigate to
Development
in the main navigation bar.
In the left sidebar menu, navigate to
Monitoring
, then select
Logs
.
At the top, select the
name
of the app.
Use the
tabs
to view different types of requests being made to or from the app. While viewing these logs, you can click an
individual request
to view more information.
Learn more about
monitoring API usage for legacy public apps
monitoring API usage for legacy public apps
.
Navigate to header Rate limits
Navigate to header
Rate limits
The number of calls your app can make is based on your account subscription in the account it’s installed in, and whether you’ve purchased the
API limit increase
API limit increase
.
You can keep the following general limits in mind when making requests to HubSpot’s API endpoints, however some APIs may also have more specific limits listed on their relevant API guide (e.g., the
exports API
exports API
).
The following APIs have limits that are unique from or stricter than the general limits. Refer to each associated guide for more information.
Associations API
Associations API
CRM Search API
CRM Search API
GraphQL API
GraphQL API
Navigate to header Publicly distributed OAuth app limits
Navigate to header
Publicly distributed OAuth app limits
For
legacy public apps
legacy public apps
, and apps on the latest versions of the developer platform (2025.2 and 2026.03) using
OAuth authentication
OAuth authentication
distributed
distributed
via the HubSpot marketplace, each HubSpot account that installs your app is limited to 110 requests every 10 seconds. This excludes the
CRM Search API
CRM Search API
.
The
API limit increase add-on
API limit increase add-on
does
not
increase the limits of
legacy public apps
legacy public apps
nor apps on the latest versions of the developer platform (2025.2 and 2026.03) using
OAuth authentication
OAuth authentication
that are
distributed
distributed
via the HubSpot marketplace.
Navigate to header Privately distributed app limits
Navigate to header
Privately distributed app limits
The limits in the table below apply to
legacy private apps
legacy private apps
, as well as apps on the latest versions of the developer platform (2025.2 and 2026.03) with a
distribution
distribution
set to
private
. Note that for these privately distributed app types:
The burst limit, detailed in the
Per 10 seconds
column below, applies individually per app.
The daily limit, detailed in the
Per day
column below, is shared across all apps within the same HubSpot account.
Product Tier
Per 10 Seconds
Per Day
Privately distributed apps
(Any Hub)
Free and Starter
100 / app
250,000 / account
(Any Hub)
Professional
190 / app
625,000 / account
(Any Hub)
Enterprise
190 / app
1,000,000 / account
Privately distributed apps with
API Limit Increase
API Limit Increase
(Any Hub)
Free, Starter, Professional, and Enterprise
250 / app
1,000,000 / account on top of your base subscription, for each limit increase. You can purchase a maximum of two API limit increases.
Privately distributed apps
Privately distributed apps with
API Limit Increase
API Limit Increase
Product Tier
(Any Hub)
Free and Starter
(Any Hub)
Professional
(Any Hub)
Enterprise
(Any Hub)
Free, Starter, Professional, and Enterprise
Per 10 Seconds
100 / app
190 / app
190 / app
250 / app
Per Day
250,000 / account
625,000 / account
1,000,000 / account
1,000,000 / account on top of your base subscription, for each limit increase. You can purchase a maximum of two API limit increases.
Navigate to header Exemptions
Navigate to header
Exemptions
Some APIs are exempt from the 10-second and daily limits (e.g., certain
marketing single send API
marketing single send API
and
source code API
source code API
endpoints). However, you should note the following for the exempt APIs:
API requests that are exempt from daily or secondary limits will
not
be logged in HubSpot. If you want to store these exempted requests, you’ll need to log these requests externally.
A high number of requests may result in
5xx
errors. These can be addressed the same as you would
429 errors
429
errors
.
Navigate to header App and account limits
Navigate to header
App and account limits
The following limits apply for creating legacy apps, webhook subscriptions, and CRM extension settings.
You can create up to 100
legacy public apps
legacy public apps
per developer account.
You can create up to 20
legacy private apps
legacy private apps
per HubSpot account.
You can create up to 1,000
webhook subscriptions
webhook subscriptions
per app.
You can create up to 25 CRM extension settings per
legacy public app
legacy public app
.
Navigate to header API-specific limits
Navigate to header
API-specific limits
Some features and APIs have more limits that are listed below. Additional limis for each API can also be found on an API’s associated guide (e.g., the
exports API
exports API
).
Navigate to header Timeline event limits
Navigate to header
Timeline event limits
Timeline events
Timeline events
in a legacy public app are subject to the following limits:
You can create up to 750 timeline event types per public app.
You can create up to 500 properties per timeline event type.
Each serialized event instance has the following size limits:
500 bytes for the event instance ID
510 KB per property/token
1 MB in total size for the event instance
Navigate to header Custom event limits
Navigate to header
Custom event limits
Custom events
Custom events
are subject to the following limits:
There is a limit of 500 unique event definitions per account.
There is a limit of 30 million event completions per month.
The send custom event completions endpoint supports up to 1250 requests per second.
The custom event completion batch endpoint supports batches of 500.
Consult the other custom event restrictions in the
API guide
API guide
.
Navigate to header Service limits
Navigate to header
Service limits
Learn more about service limits and pricing on
HubSpot’s Product & Services Catalog
HubSpot’s Product & Services Catalog
.
Navigate to header Rate limit error responses
Navigate to header
Rate limit error responses
Any app or integration exceeding its rate limits will receive a
429
error response for all subsequent API calls. Requests resulting in an error response shouldn’t exceed 5% of your total daily requests. If you plan on listing your app in the
HubSpot Marketplace
HubSpot Marketplace
, it must stay under this 5% limit to be certified.
The
429
response will have the following format:
Report incorrect code
Copy the contents from the code block
Ask AI
{
"status"
:
"error"
,
"message"
:
"You have reached your daily limit."
,
"errorType"
:
"RATE_LIMIT"
,
"correlationId"
:
"c033cdaa-2c40-4a64-ae48-b4cec88dad24"
,
"policyName"
:
"DAILY"
,
"requestId"
:
"3d3e35b7-0dae-4b9f-a6e3-9c230cbcf8dd"
}
The
message
and
policyName
will indicate which limit you hit (either daily or secondly).
The
daily
limit resets at midnight based on your
time zone setting
time zone setting
.
The following table details the rate limit headers included in the response of each API request to HubSpot, subject to the exceptions listed below the table.
Header
Description
X-HubSpot-RateLimit-Daily
The number of API requests that are allowed per day. Note that this header is not included in the response to API requests authorized using
OAuth
OAuth
.
X-HubSpot-RateLimit-Daily-Remaining
The number of API requests still allowed for the current day. Note that this header is not included in the response to API requests authorized using
OAuth
OAuth
.
X-HubSpot-RateLimit-Interval-Milliseconds
The window of time that the
X-HubSpot-RateLimit-Max
and
X-HubSpot-RateLimit-Remaining
headers apply to.For example, a value of 10000 would be a window of 10 seconds.
X-HubSpot-RateLimit-Max
The number of requests allowed in the window specified in
X-HubSpot-RateLimit-Interval-Milliseconds
.For example, if this header had a value of 100, and the
X-HubSpot-RateLimit-Interval-Milliseconds
header was 10000, the enforced limit would be 100 requests per 10 seconds.
X-HubSpot-RateLimit-Remaining
The number of API requests still allowed for the window specified in
X-HubSpot-RateLimit-Interval-Milliseconds
.
Header
X-HubSpot-RateLimit-Daily
X-HubSpot-RateLimit-Daily-Remaining
X-HubSpot-RateLimit-Interval-Milliseconds
X-HubSpot-RateLimit-Max
X-HubSpot-RateLimit-Remaining
Description
The number of API requests that are allowed per day. Note that this header is not included in the response to API requests authorized using
OAuth
OAuth
.
The number of API requests still allowed for the current day. Note that this header is not included in the response to API requests authorized using
OAuth
OAuth
.
The window of time that the
X-HubSpot-RateLimit-Max
and
X-HubSpot-RateLimit-Remaining
headers apply to.For example, a value of 10000 would be a window of 10 seconds.
The number of requests allowed in the window specified in
X-HubSpot-RateLimit-Interval-Milliseconds
.For example, if this header had a value of 100, and the
X-HubSpot-RateLimit-Interval-Milliseconds
header was 10000, the enforced limit would be 100 requests per 10 seconds.
The number of API requests still allowed for the window specified in
X-HubSpot-RateLimit-Interval-Milliseconds
.
The
X-HubSpot-RateLimit-Secondly
and
X-HubSpot-RateLimit-Secondly-Remaining
headers are still included and will still have accurate data, but the limit referenced by these headers is no longer enforced and these two headers should be considered deprecated.
Responses from the search API endpoints will
not
include any of the rate limit headers listed above.
You can also check the number of calls used during the current day using
this endpoint
this endpoint
.
Navigate to header Address rate limit issues
Navigate to header
Address rate limit issues
The sections below detail two possible ways you can reduce the volume of API traffic if you repeatedly hit your API request limit. If you’re running into the
TEN_SECONDLY_ROLLING
limit, you should throttle the requests that your app is making to stay under that limit. In addition to throttling the requests, or if you’re running into the daily limit, check out the suggestions below....
|
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allowed per day. Note that this header is not included in the response to API requests authorized using","depth":14,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"OAuth","depth":14,"on_screen":false,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"OAuth","depth":15,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":".","depth":14,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Daily-Remaining","depth":15,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"The number of API requests still allowed for the current day. Note that this header is not included in the response to API requests authorized using","depth":14,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"OAuth","depth":14,"on_screen":false,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"OAuth","depth":15,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":".","depth":14,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Interval-Milliseconds","depth":15,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"The window of time that the","depth":14,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Max","depth":15,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"and","depth":14,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":15,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"headers apply to.For example, a value of 10000 would be a window of 10 seconds.","depth":14,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Max","depth":15,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"The number of requests allowed in the window specified in","depth":14,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Interval-Milliseconds","depth":15,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":".For example, if this header had a value of 100, and the","depth":14,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Interval-Milliseconds","depth":15,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"header was 10000, the enforced limit would be 100 requests per 10 seconds.","depth":14,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":15,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"The number of API requests still allowed for the window specified in","depth":14,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Interval-Milliseconds","depth":15,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":".","depth":14,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Header","depth":15,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Daily","depth":15,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Daily-Remaining","depth":15,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Interval-Milliseconds","depth":15,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Max","depth":15,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":15,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Description","depth":15,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"The number of API requests that are allowed per day. Note that this header is not included in the response to API requests authorized using","depth":14,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"OAuth","depth":14,"on_screen":false,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"OAuth","depth":15,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":".","depth":14,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"The number of API requests still allowed for the current day. Note that this header is not included in the response to API requests authorized using","depth":14,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"OAuth","depth":14,"on_screen":false,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"OAuth","depth":15,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":".","depth":14,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"The window of time that the","depth":14,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Max","depth":15,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"and","depth":14,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":15,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"headers apply to.For example, a value of 10000 would be a window of 10 seconds.","depth":14,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"The number of requests allowed in the window specified in","depth":14,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Interval-Milliseconds","depth":15,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":".For example, if this header had a value of 100, and the","depth":14,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Interval-Milliseconds","depth":15,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"header was 10000, the enforced limit would be 100 requests per 10 seconds.","depth":14,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"The number of API requests still allowed for the window specified in","depth":14,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Interval-Milliseconds","depth":15,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":".","depth":14,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"The","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Secondly","depth":13,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"and","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Secondly-Remaining","depth":13,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"headers are still included and will still have accurate data, but the limit referenced by these headers is no longer enforced and these two headers should be considered deprecated.","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Responses from the search API endpoints will","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"not","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"include any of the rate limit headers listed above.","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"You can also check the number of calls used during the current day using","depth":10,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"this endpoint","depth":10,"on_screen":false,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"this endpoint","depth":11,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":".","depth":10,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXHeading","text":"Navigate to header Address rate limit issues","depth":9,"on_screen":false,"help_text":"","role_description":"heading","subrole":"AXUnknown"},{"role":"AXLink","text":"Navigate to header","depth":11,"on_screen":false,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Address rate limit issues","depth":11,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"The sections below detail two possible ways you can reduce the volume of API traffic if you repeatedly hit your API request limit. If you’re running into the","depth":10,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"TEN_SECONDLY_ROLLING","depth":11,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"limit, you should throttle the requests that your app is making to stay under that limit. In addition to throttling the requests, or if you’re running into the daily limit, check out the suggestions below.","depth":10,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"}]...
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Platform Sprint 3 Q2 - Platform Team - Scrum Board Platform Sprint 3 Q2 - Platform Team - Scrum Board - Jira
Platform Sprint 3 Q2 - Platform Team - Scrum Board - Jira
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
Service-Desk - Queues - Platform team - Service space - Jira
Service-Desk - Queues - Platform team - Service space - Jira
Jy 20807 check various issues with stages by nikolaybiaivanov · Pull Request #12041 · jiminny/app
Jy 20807 check various issues with stages by nikolaybiaivanov · Pull Request #12041 · jiminny/app
Sentry
Sentry
Pull requests · jiminny/app
Pull requests · jiminny/app
Userpilot | Ask Jiminny Report Generated
Userpilot | Ask Jiminny Report Generated
JY-20773 fix user pilot tracking ofr automated report generated by LakyLak · Pull Request #12024 · jiminny/app
JY-20773 fix user pilot tracking ofr automated report generated by LakyLak · Pull Request #12024 · jiminny/app
Jiminny
Jiminny
API usage guidelines and limits - HubSpot docs
API usage guidelines and limits - HubSpot docs
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Authentication and security
Authentication and security
Checking API usage
Checking API usage
Apps built on version 2025.2 and 2026.03 of the developer platform
Apps built on version 2025.2 and 2026.03 of the developer platform
Legacy apps
Legacy apps
Rate limits
Rate limits
Publicly distributed OAuth app limits
Publicly distributed OAuth app limits
Privately distributed app limits
Privately distributed app limits
Exemptions
Exemptions
App and account limits
App and account limits
API-specific limits
API-specific limits
Timeline event limits
Timeline event limits
Custom event limits
Custom event limits
Service limits
Service limits
Rate limit error responses
Rate limit error responses
Address rate limit issues
Address rate limit issues
Use batch APIs and cache results when possible
Use batch APIs and cache results when possible
Use webhooks to get updated data from HubSpot
Use webhooks to get updated data from HubSpot
Platform
API usage guidelines and limits
API usage guidelines and limits
Learn about the different API and developer feature limits in HubSpot.
Documentation Index
Documentation Index
Fetch the complete documentation index at:
https://developers.hubspot.com/docs/llms.txt
https://developers.hubspot.com/docs/llms.txt
Use this file to discover all available pages before exploring further.
HubSpot closely monitors usage of all APIs to ensure a quality experience for every user. All app and integration developers must comply with the
HubSpot Acceptable Use Policy
HubSpot Acceptable Use Policy
and
API Terms
API Terms
. While HubSpot reserves the right to change or deprecate the APIs over time, updates will always be provided in advance through the
Developer Changelog
Developer Changelog
.
Navigate to header Authentication and security
Navigate to header
Authentication and security
For optimal security, all apps must use HubSpot’s
OAuth protocol
OAuth protocol
directly, or use your app’s static auth token if you’re building an
app installed in a single account
app installed in a single account
. Apps are responsible for storing time-to-live (TTL) data and refreshing user access tokens in accordance with this protocol. When an access token is generated, it will include an
expires_in
parameter indicating how long it can be used to make API calls before refreshing.
Unauthorized (401)
requests are not a valid indicator that a new access token must be retrieved.
Navigate to header Checking API usage
Navigate to header
Checking API usage
Navigate to header Apps built on version 2025.2 and 2026.03 of the developer platform
Navigate to header
Apps built on version 2025.2 and 2026.03 of the developer platform
To view API usage for your apps built on the
new developer platform
new developer platform
:
In your HubSpot account, navigate to
Development
in the main navigation bar.
In the left sidebar menu, navigate to
Monitoring
>
API call usage
.
Your API usage across all your apps will be listed at the top of the page. You can also review usage for the apps you built in your specific account, along with any third-party apps you’ve installed.
Navigate to header Legacy apps
Navigate to header
Legacy apps
To view API usage for a legacy
private app
private app
:
In your HubSpot account, navigate to
Development
in the main navigation bar.
In the left sidebar menu, navigate to
Legacy apps
.
Click the
name
of the private app.
On the app details page, click the
Logs
tab.
Review the API calls listed in the table. You can also use the
search bar
,
filters
, and
date pickers
to further refine the displayed API calls.
Learn more about
checking API usage in legacy private apps
checking API usage in legacy private apps
.
To view API usage for a public app using OAuth:
In your HubSpot account, navigate to
Development
in the main navigation bar.
In the left sidebar menu, navigate to
Monitoring
, then select
Logs
.
At the top, select the
name
of the app.
Use the
tabs
to view different types of requests being made to or from the app. While viewing these logs, you can click an
individual request
to view more information.
Learn more about
monitoring API usage for legacy public apps
monitoring API usage for legacy public apps
.
Navigate to header Rate limits
Navigate to header
Rate limits
The number of calls your app can make is based on your account subscription in the account it’s installed in, and whether you’ve purchased the
API limit increase
API limit increase
.
You can keep the following general limits in mind when making requests to HubSpot’s API endpoints, however some APIs may also have more specific limits listed on their relevant API guide (e.g., the
exports API
exports API
).
The following APIs have limits that are unique from or stricter than the general limits. Refer to each associated guide for more information.
Associations API
Associations API
CRM Search API
CRM Search API
GraphQL API
GraphQL API
Navigate to header Publicly distributed OAuth app limits
Navigate to header
Publicly distributed OAuth app limits
For
legacy public apps
legacy public apps
, and apps on the latest versions of the developer platform (2025.2 and 2026.03) using
OAuth authentication
OAuth authentication
distributed
distributed
via the HubSpot marketplace, each HubSpot account that installs your app is limited to 110 requests every 10 seconds. This excludes the
CRM Search API
CRM Search API
.
The
API limit increase add-on
API limit increase add-on
does
not
increase the limits of
legacy public apps
legacy public apps
nor apps on the latest versions of the developer platform (2025.2 and 2026.03) using
OAuth authentication
OAuth authentication
that are
distributed
distributed
via the HubSpot marketplace.
Navigate to header Privately distributed app limits
Navigate to header
Privately distributed app limits
The limits in the table below apply to
legacy private apps
legacy private apps
, as well as apps on the latest versions of the developer platform (2025.2 and 2026.03) with a
distribution
distribution
set to
private
. Note that for these privately distributed app types:
The burst limit, detailed in the
Per 10 seconds
column below, applies individually per app.
The daily limit, detailed in the
Per day
column below, is shared across all apps within the same HubSpot account.
Product Tier
Per 10 Seconds
Per Day
Privately distributed apps
(Any Hub)
Free and Starter
100 / app
250,000 / account
(Any Hub)
Professional
190 / app
625,000 / account
(Any Hub)
Enterprise
190 / app
1,000,000 / account
Privately distributed apps with
API Limit Increase
API Limit Increase
(Any Hub)
Free, Starter, Professional, and Enterprise
250 / app
1,000,000 / account on top of your base subscription, for each limit increase. You can purchase a maximum of two API limit increases.
Privately distributed apps
Privately distributed apps with
API Limit Increase
API Limit Increase
Product Tier
(Any Hub)
Free and Starter
(Any Hub)
Professional
(Any Hub)
Enterprise
(Any Hub)
Free, Starter, Professional, and Enterprise
Per 10 Seconds
100 / app
190 / app
190 / app
250 / app
Per Day
250,000 / account
625,000 / account
1,000,000 / account
1,000,000 / account on top of your base subscription, for each limit increase. You can purchase a maximum of two API limit increases.
Navigate to header Exemptions
Navigate to header
Exemptions
Some APIs are exempt from the 10-second and daily limits (e.g., certain
marketing single send API
marketing single send API
and
source code API
source code API
endpoints). However, you should note the following for the exempt APIs:
API requests that are exempt from daily or secondary limits will
not
be logged in HubSpot. If you want to store these exempted requests, you’ll need to log these requests externally.
A high number of requests may result in
5xx
errors. These can be addressed the same as you would
429 errors
429
errors
.
Navigate to header App and account limits
Navigate to header
App and account limits
The following limits apply for creating legacy apps, webhook subscriptions, and CRM extension settings.
You can create up to 100
legacy public apps
legacy public apps
per developer account.
You can create up to 20
legacy private apps
legacy private apps
per HubSpot account.
You can create up to 1,000
webhook subscriptions
webhook subscriptions
per app.
You can create up to 25 CRM extension settings per
legacy public app
legacy public app
.
Navigate to header API-specific limits
Navigate to header
API-specific limits
Some features and APIs have more limits that are listed below. Additional limis for each API can also be found on an API’s associated guide (e.g., the
exports API
exports API
).
Navigate to header Timeline event limits
Navigate to header
Timeline event limits
Timeline events
Timeline events
in a legacy public app are subject to the following limits:
You can create up to 750 timeline event types per public app.
You can create up to 500 properties per timeline event type.
Each serialized event instance has the following size limits:
500 bytes for the event instance ID
510 KB per property/token
1 MB in total size for the event instance
Navigate to header Custom event limits
Navigate to header
Custom event limits
Custom events
Custom events
are subject to the following limits:
There is a limit of 500 unique event definitions per account.
There is a limit of 30 million event completions per month.
The send custom event completions endpoint supports up to 1250 requests per second.
The custom event completion batch endpoint supports batches of 500.
Consult the other custom event restrictions in the
API guide
API guide
.
Navigate to header Service limits
Navigate to header
Service limits
Learn more about service limits and pricing on
HubSpot’s Product & Services Catalog
HubSpot’s Product & Services Catalog
.
Navigate to header Rate limit error responses
Navigate to header
Rate limit error responses
Any app or integration exceeding its rate limits will receive a
429
error response for all subsequent API calls. Requests resulting in an error response shouldn’t exceed 5% of your total daily requests. If you plan on listing your app in the
HubSpot Marketplace
HubSpot Marketplace
, it must stay under this 5% limit to be certified.
The
429
response will have the following format:
Report incorrect code
Copy the contents from the code block
Ask AI
{
"status"
:
"error"
,
"message"
:
"You have reached your daily limit."
,
"errorType"
:
"RATE_LIMIT"
,
"correlationId"
:
"c033cdaa-2c40-4a64-ae48-b4cec88dad24"
,
"policyName"
:
"DAILY"
,
"requestId"
:
"3d3e35b7-0dae-4b9f-a6e3-9c230cbcf8dd"
}
The
message
and
policyName
will indicate which limit you hit (either daily or secondly).
The
daily
limit resets at midnight based on your
time zone setting
time zone setting
.
The following table details the rate limit headers included in the response of each API request to HubSpot, subject to the exceptions listed below the table.
Header
Description
X-HubSpot-RateLimit-Daily
The number of API requests that are allowed per day. Note that this header is not included in the response to API requests authorized using
OAuth
OAuth
.
X-HubSpot-RateLimit-Daily-Remaining
The number of API requests still allowed for the current day. Note that this header is not included in the response to API requests authorized using
OAuth
OAuth
.
X-HubSpot-RateLimit-Interval-Milliseconds
The window of time that the
X-HubSpot-RateLimit-Max
and
X-HubSpot-RateLimit-Remaining
headers apply to.For example, a value of 10000 would be a window of 10 seconds.
X-HubSpot-RateLimit-Max
The number of requests allowed in the window specified in
X-HubSpot-RateLimit-Interval-Milliseconds
.For example, if this header had a value of 100, and the
X-HubSpot-RateLimit-Interval-Milliseconds
header was 10000, the enforced limit would be 100 requests per 10 seconds.
X-HubSpot-RateLimit-Remaining
The number of API requests still allowed for the window specified in
X-HubSpot-RateLimit-Interval-Milliseconds
.
Header
X-HubSpot-RateLimit-Daily
X-HubSpot-RateLimit-Daily-Remaining
X-HubSpot-RateLimit-Interval-Milliseconds
X-HubSpot-RateLimit-Max
X-HubSpot-RateLimit-Remaining
Description
The number of API requests that are allowed per day. Note that this header is not included in the response to API requests authorized using
OAuth
OAuth
.
The number of API requests still allowed for the current day. Note that this header is not included in the response to API requests authorized using
OAuth
OAuth
.
The window of time that the
X-HubSpot-RateLimit-Max
and
X-HubSpot-RateLimit-Remaining
headers apply to.For example, a value of 10000 would be a window of 10 seconds.
The number of requests allowed in the window specified in
X-HubSpot-RateLimit-Interval-Milliseconds
.For example, if this header had a value of 100, and the
X-HubSpot-RateLimit-Interval-Milliseconds
header was 10000, the enforced limit would be 100 requests per 10 seconds.
The number of API requests still allowed for the window specified in
X-HubSpot-RateLimit-Interval-Milliseconds
.
The
X-HubSpot-RateLimit-Secondly
and
X-HubSpot-RateLimit-Secondly-Remaining
headers are still included and will still have accurate data, but the limit referenced by these headers is no longer enforced and these two headers should be considered deprecated.
Responses from the search API endpoints will
not
include any of the rate limit headers listed above.
You can also check the number of calls used during the current day using
this endpoint
this endpoint
.
Navigate to header Address rate limit issues
Navigate to header
Address rate limit issues
The sections below detail two possible ways you can reduce the volume of API traffic if you repeatedly hit your API request limit. If you’re running into the
TEN_SECONDLY_ROLLING
limit, you should throttle the requests that your app is making to stay under that limit. In addition to throttling the requests, or if you’re running into the daily limit, check out the suggestions below....
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Platform Sprint 3 Q2 - Platform Team - Scrum Board Platform Sprint 3 Q2 - Platform Team - Scrum Board - Jira
Platform Sprint 3 Q2 - Platform Team - Scrum Board - Jira
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
Service-Desk - Queues - Platform team - Service space - Jira
Service-Desk - Queues - Platform team - Service space - Jira
Jy 20807 check various issues with stages by nikolaybiaivanov · Pull Request #12041 · jiminny/app
Jy 20807 check various issues with stages by nikolaybiaivanov · Pull Request #12041 · jiminny/app
Sentry
Sentry
Pull requests · jiminny/app
Pull requests · jiminny/app
Userpilot | Ask Jiminny Report Generated
Userpilot | Ask Jiminny Report Generated
JY-20773 fix user pilot tracking ofr automated report generated by LakyLak · Pull Request #12024 · jiminny/app
JY-20773 fix user pilot tracking ofr automated report generated by LakyLak · Pull Request #12024 · jiminny/app
Jiminny
Jiminny
API usage guidelines and limits - HubSpot docs
API usage guidelines and limits - HubSpot docs
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Authentication and security
Authentication and security
Checking API usage
Checking API usage
Apps built on version 2025.2 and 2026.03 of the developer platform
Apps built on version 2025.2 and 2026.03 of the developer platform
Legacy apps
Legacy apps
Rate limits
Rate limits
Publicly distributed OAuth app limits
Publicly distributed OAuth app limits
Privately distributed app limits
Privately distributed app limits
Exemptions
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App and account limits
App and account limits
API-specific limits
API-specific limits
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Timeline event limits
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Service limits
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Rate limit error responses
Rate limit error responses
Address rate limit issues
Address rate limit issues
Use batch APIs and cache results when possible
Use batch APIs and cache results when possible
Use webhooks to get updated data from HubSpot
Use webhooks to get updated data from HubSpot
Platform
API usage guidelines and limits
API usage guidelines and limits
Learn about the different API and developer feature limits in HubSpot.
Documentation Index
Documentation Index
Fetch the complete documentation index at:
https://developers.hubspot.com/docs/llms.txt
https://developers.hubspot.com/docs/llms.txt
Use this file to discover all available pages before exploring further.
HubSpot closely monitors usage of all APIs to ensure a quality experience for every user. All app and integration developers must comply with the
HubSpot Acceptable Use Policy
HubSpot Acceptable Use Policy
and
API Terms
API Terms
. While HubSpot reserves the right to change or deprecate the APIs over time, updates will always be provided in advance through the
Developer Changelog
Developer Changelog
.
Navigate to header Authentication and security
Navigate to header
Authentication and security
For optimal security, all apps must use HubSpot’s
OAuth protocol
OAuth protocol
directly, or use your app’s static auth token if you’re building an
app installed in a single account
app installed in a single account
. Apps are responsible for storing time-to-live (TTL) data and refreshing user access tokens in accordance with this protocol. When an access token is generated, it will include an
expires_in
parameter indicating how long it can be used to make API calls before refreshing.
Unauthorized (401)
requests are not a valid indicator that a new access token must be retrieved.
Navigate to header Checking API usage
Navigate to header
Checking API usage
Navigate to header Apps built on version 2025.2 and 2026.03 of the developer platform
Navigate to header
Apps built on version 2025.2 and 2026.03 of the developer platform
To view API usage for your apps built on the
new developer platform
new developer platform
:
In your HubSpot account, navigate to
Development
in the main navigation bar.
In the left sidebar menu, navigate to
Monitoring
>
API call usage
.
Your API usage across all your apps will be listed at the top of the page. You can also review usage for the apps you built in your specific account, along with any third-party apps you’ve installed.
Navigate to header Legacy apps
Navigate to header
Legacy apps
To view API usage for a legacy
private app
private app
:
In your HubSpot account, navigate to
Development
in the main navigation bar.
In the left sidebar menu, navigate to
Legacy apps
.
Click the
name
of the private app.
On the app details page, click the
Logs
tab.
Review the API calls listed in the table. You can also use the
search bar
,
filters
, and
date pickers
to further refine the displayed API calls.
Learn more about
checking API usage in legacy private apps
checking API usage in legacy private apps
.
To view API usage for a public app using OAuth:
In your HubSpot account, navigate to
Development
in the main navigation bar.
In the left sidebar menu, navigate to
Monitoring
, then select
Logs
.
At the top, select the
name
of the app.
Use the
tabs
to view different types of requests being made to or from the app. While viewing these logs, you can click an
individual request
to view more information.
Learn more about
monitoring API usage for legacy public apps
monitoring API usage for legacy public apps
.
Navigate to header Rate limits
Navigate to header
Rate limits
The number of calls your app can make is based on your account subscription in the account it’s installed in, and whether you’ve purchased the
API limit increase
API limit increase
.
You can keep the following general limits in mind when making requests to HubSpot’s API endpoints, however some APIs may also have more specific limits listed on their relevant API guide (e.g., the
exports API
exports API
).
The following APIs have limits that are unique from or stricter than the general limits. Refer to each associated guide for more information.
Associations API
Associations API
CRM Search API
CRM Search API
GraphQL API
GraphQL API
Navigate to header Publicly distributed OAuth app limits
Navigate to header
Publicly distributed OAuth app limits
For
legacy public apps
legacy public apps
, and apps on the latest versions of the developer platform (2025.2 and 2026.03) using
OAuth authentication
OAuth authentication
distributed
distributed
via the HubSpot marketplace, each HubSpot account that installs your app is limited to 110 requests every 10 seconds. This excludes the
CRM Search API...
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This excludes the","depth":10,"bounds":{"left":0.57629657,"top":0.5482841,"width":0.23454122,"height":0.038707104},"on_screen":true,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"CRM Search API","depth":10,"bounds":{"left":0.57629657,"top":0.5714286,"width":0.24019282,"height":0.03790902},"on_screen":true,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false}]...
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Platform Sprint 3 Q2 - Platform Team - Scrum Board Platform Sprint 3 Q2 - Platform Team - Scrum Board - Jira
Platform Sprint 3 Q2 - Platform Team - Scrum Board - Jira
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
Service-Desk - Queues - Platform team - Service space - Jira
Service-Desk - Queues - Platform team - Service space - Jira
Jy 20807 check various issues with stages by nikolaybiaivanov · Pull Request #12041 · jiminny/app
Jy 20807 check various issues with stages by nikolaybiaivanov · Pull Request #12041 · jiminny/app
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Pull requests · jiminny/app
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Userpilot | Ask Jiminny Report Generated
Userpilot | Ask Jiminny Report Generated
JY-20773 fix user pilot tracking ofr automated report generated by LakyLak · Pull Request #12024 · jiminny/app
JY-20773 fix user pilot tracking ofr automated report generated by LakyLak · Pull Request #12024 · jiminny/app
Jiminny
Jiminny
API usage guidelines and limits - HubSpot docs
API usage guidelines and limits - HubSpot docs
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Authentication and security
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Checking API usage
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Apps built on version 2025.2 and 2026.03 of the developer platform
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Legacy apps
Legacy apps
Rate limits
Rate limits
Publicly distributed OAuth app limits
Publicly distributed OAuth app limits
Privately distributed app limits
Privately distributed app limits
Exemptions
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Address rate limit issues
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Use batch APIs and cache results when possible
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Use webhooks to get updated data from HubSpot
Platform
API usage guidelines and limits
API usage guidelines and limits
Learn about the different API and developer feature limits in HubSpot.
Documentation Index
Documentation Index
Fetch the complete documentation index at:
https://developers.hubspot.com/docs/llms.txt
https://developers.hubspot.com/docs/llms.txt
Use this file to discover all available pages before exploring further.
HubSpot closely monitors usage of all APIs to ensure a quality experience for every user. All app and integration developers must comply with the
HubSpot Acceptable Use Policy
HubSpot Acceptable Use Policy
and
API Terms
API Terms
. While HubSpot reserves the right to change or deprecate the APIs over time, updates will always be provided in advance through the
Developer Changelog
Developer Changelog
.
Navigate to header Authentication and security
Navigate to header
Authentication and security
For optimal security, all apps must use HubSpot’s
OAuth protocol
OAuth protocol
directly, or use your app’s static auth token if you’re building an
app installed in a single account
app installed in a single account
. Apps are responsible for storing time-to-live (TTL) data and refreshing user access tokens in accordance with this protocol. When an access token is generated, it will include an
expires_in
parameter indicating how long it can be used to make API calls before refreshing.
Unauthorized (401)
requests are not a valid indicator that a new access token must be retrieved.
Navigate to header Checking API usage
Navigate to header
Checking API usage
Navigate to header Apps built on version 2025.2 and 2026.03 of the developer platform
Navigate to header
Apps built on version 2025.2 and 2026.03 of the developer platform
To view API usage for your apps built on the
new developer platform
new developer platform
:
In your HubSpot account, navigate to
Development
in the main navigation bar.
In the left sidebar menu, navigate to
Monitoring
>
API call usage
.
Your API usage across all your apps will be listed at the top of the page. You can also review usage for the apps you built in your specific account, along with any third-party apps you’ve installed.
Navigate to header Legacy apps
Navigate to header
Legacy apps
To view API usage for a legacy
private app
private app
:
In your HubSpot account, navigate to
Development
in the main navigation bar.
In the left sidebar menu, navigate to
Legacy apps
.
Click the
name
of the private app.
On the app details page, click the
Logs
tab.
Review the API calls listed in the table. You can also use the
search bar
,
filters
, and
date pickers
to further refine the displayed API calls.
Learn more about
checking API usage in legacy private apps
checking API usage in legacy private apps
.
To view API usage for a public app using OAuth:
In your HubSpot account, navigate to
Development
in the main navigation bar.
In the left sidebar menu, navigate to
Monitoring
, then select
Logs
.
At the top, select the
name
of the app.
Use the
tabs
to view different types of requests being made to or from the app. While viewing these logs, you can click an
individual request
to view more information.
Learn more about
monitoring API usage for legacy public apps
monitoring API usage for legacy public apps
.
Navigate to header Rate limits
Navigate to header
Rate limits
The number of calls your app can make is based on your account subscription in the account it’s installed in, and whether you’ve purchased the
API limit increase
API limit increase
.
You can keep the following general limits in mind when making requests to HubSpot’s API endpoints, however some APIs may also have more specific limits listed on their relevant API guide (e.g., the
exports API
exports API
).
The following APIs have limits that are unique from or stricter than the general limits. Refer to each associated guide for more information.
Associations API
Associations API
CRM Search API
CRM Search API
GraphQL API
GraphQL API
Navigate to header Publicly distributed OAuth app limits
Navigate to header
Publicly distributed OAuth app limits
For
legacy public apps
legacy public apps
, and apps on the latest versions of the developer platform (2025.2 and 2026.03) using
OAuth authentication
OAuth authentication
distributed
distributed
via the HubSpot marketplace, each HubSpot account that installs your app is limited to 110 requests every 10 seconds. This excludes the
CRM Search API...
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Associate records - HubSpot docs — Work
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Platform Sprint 3 Q2 - Platform Team - Scrum Board Platform Sprint 3 Q2 - Platform Team - Scrum Board - Jira
Platform Sprint 3 Q2 - Platform Team - Scrum Board - Jira
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
Service-Desk - Queues - Platform team - Service space - Jira
Service-Desk - Queues - Platform team - Service space - Jira
Jy 20807 check various issues with stages by nikolaybiaivanov · Pull Request #12041 · jiminny/app
Jy 20807 check various issues with stages by nikolaybiaivanov · Pull Request #12041 · jiminny/app
Sentry
Sentry
Pull requests · jiminny/app
Pull requests · jiminny/app
Userpilot | Ask Jiminny Report Generated
Userpilot | Ask Jiminny Report Generated
JY-20773 fix user pilot tracking ofr automated report generated by LakyLak · Pull Request #12024 · jiminny/app
JY-20773 fix user pilot tracking ofr automated report generated by LakyLak · Pull Request #12024 · jiminny/app
Jiminny
Jiminny
Associate records - HubSpot docs
Associate records - HubSpot docs
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Associate records
API Guide
API Guide
POST Report high usage
POST
Report high usage
PUT Associate records (labeled)
PUT
Associate records (labeled)
PUT Associate records (default)
PUT
Associate records (default)
DEL Remove association between records
DEL
Remove association between records
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Retrieve events
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Associate records
Associate records
Associate records without a label
Associate records without a label
Associate records with a label
Associate records with a label
Retrieve associated records
Retrieve associated records
Update record association labels
Update record association labels
Remove record associations
Remove record associations
Remove all associations between records
Remove all associations between records
Remove associations with specific labels
Remove associations with specific labels
Report on high association usage
Report on high association usage
Limits
Limits
Association type ID values
Association type ID values
Company to object
Company to object
Contact to object
Contact to object
Deal to object
Deal to object
Ticket to object
Ticket to object
Lead to object
Lead to object
Appointment to object
Appointment to object
Goal to object
Goal to object
Course to object
Course to object
Listing to object
Listing to object
Service to object
Service to object
Call to object
Call to object
Email to object
Email to object
Feedback submission to object
Feedback submission to object
Meeting to object
Meeting to object
Note to object
Note to object
Postal mail to object
Postal mail to object
Task to object
Task to object
Communication (SMS, WhatsApp, or LinkedIn message) to object
Communication (SMS, WhatsApp, or LinkedIn message) to object
Invoice to object
Invoice to object
Payment to object
Payment to object
Payment link to object
Payment link to object
Quote to object
Quote to object
Line item to object
Line item to object
Order to object
Order to object
Cart to object
Cart to object
Discount to object
Discount to object
Fee to object
Fee to object
Subscription to object
Subscription to object
Tax to object
Tax to object
Project to object
Project to object
v1 associations (legacy)
v1 associations (legacy)
Associate records
Associate records
Associate records
Use the associations API to create, manage, and remove associations between CRM records.
Documentation Index
Documentation Index
Fetch the complete documentation index at:
https://developers.hubspot.com/docs/llms.txt
https://developers.hubspot.com/docs/llms.txt
Use this file to discover all available pages before exploring further.
For the previous version, please see the documentation for the
v3 Associations API
v3 Associations API
.
Scope requirements
Scope requirements
Associations represent the relationships between objects and activities in the HubSpot CRM. Record associations can exist between records of different objects (e.g., Contact to Company), as well as within the same object (e.g., Company to Company).
The Associations API includes two sets of endpoints:
Association details endpoints
: create, edit, and remove associations between records. In this article, you’ll learn more about using the associations details endpoints.
Association schema endpoints
Association schema endpoints
: view your account’s association definitions (also known as types), create and manage custom association labels, and set limits for associations. Learn more about using the
associations schema endpoints
associations schema endpoints
.
Learn more about objects, records, properties, and associations APIs in the
Understanding the CRM
Understanding the CRM
guide.
Navigate to header Associate records
Navigate to header
Associate records
You can associate records with each other unlabeled (i.e. contact and company associated with no descriptor) or with labels (e.g., contact and company associated and the contact is the company’s
Decision maker
).
The number of associations a record can have depends on
the object and your HubSpot subscription.
the object and your HubSpot subscription.
Navigate to header Associate records without a label
Navigate to header
Associate records without a label
You can create a default unlabeled association between two records, or set up unlabeled associations for records in bulk. To set up an individual default association between two records, make a
PUT
request to
/crm/objects/2026-03/{fromObjectType}/{fromObjectId}/associations/default/{toObjectType}/{toObjectId}
In the request URL, include:
fromObjectType
:
the ID of the object you’re associating. To find the ID values, refer to this
list of object type IDs,
list of object type IDs,
or for contacts, companies, deals, tickets, and notes, you can use the object name (e.g.,
contact
,
company
).
fromObjectId
:
the ID of the record to associate.
toObjectType
:
the ID of the object you’re associating the record to. To find the ID values, refer to this
list of object type IDs,
list of object type IDs,
or for contacts, companies, deals, tickets, and notes, you can use the object name (e.g.,
contact
,
company
).
toObjectId
:
the ID of the record to associate to.
For example, to associate a contact record whose ID is
12345
with a company record whose ID is
67891
, your request URL would be:
/crm/objects/2026-03/contact/12345/associations/default/company/67891
.
To associate records without a label in bulk, make a
POST
request to
/crm/associations/2026-03/{fromObjectType}/{toObjectType}/batch/associate/default
. In the request body, include
objectId
values for the records you want to associate. For example:
Report incorrect code
Copy the contents from the code block
Ask AI
{
"inputs"
: [
{
"from"
: {
"id"
:
"12345"
},
"to"
: {
"id"
:
"56678"
}
},
{
"from"
: {
"id"
:
"49475850"
},
"to"
: {
"id"
:
"2049585"
}
},
{
"from"
: {
"id"
:
"304958"
},
"to"
: {
"id"
:
"204959"
}
}
]
}
Navigate to header Associate records with a label
Navigate to header
Associate records with a label
You can also associate records with
labels
labels
for individual record pairs or multiple pairs of records in bulk. For each association, depending on your
association limits
association limits
, you can include multiple labels.
To associate two records and set a label to describe the association, make a
PUT
request to
/crm/objects/2026-03/{objectType}/{objectId}/associations/{toObjectType}/{toObjectId}
. In the request URL, include the
id
values of the two records you’re associating.
To bulk create labeled associations between records of the same objects, make a
POST
request to
/crm/associations/2026-03/{fromObjectType}/{toObjectType}/batch/create
. In the request body, include the
id
values of records to associate in addition to the required parameters below.
In the request body, include the following information to indicate the labeled association you want to create:
associationCategory
: either
HUBSPOT_DEFINED
(default label) or
USER_DEFINED
(custom label).
associationTypeId
: the numerical ID value for the label. If using a default label (e.g., Primary company), refer to this
list of default type IDs.
list of default type IDs.
If you’re using a custom label, you’ll need to
retrieve the labels
retrieve the labels
between those objects.
For cross-object and paired label relationships, ensure you use the
typeId
that refers to the correct direction. For example, if your
fromObjectType
is
contact
and your
toObjectType
is...
|
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{","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"id\"","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":":","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"49475850\"","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"},","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"to\"","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":": {","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"id\"","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":":","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"2049585\"","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"}","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"},","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"{","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"from\"","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":": {","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"id\"","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":":","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"304958\"","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"},","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"to\"","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":": {","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"id\"","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":":","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"204959\"","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"}","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"}","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"]","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"}","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXHeading","text":"Navigate to header Associate records with a label","depth":9,"on_screen":false,"help_text":"","role_description":"heading","subrole":"AXUnknown"},{"role":"AXLink","text":"Navigate to header","depth":11,"on_screen":false,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Associate records with a label","depth":11,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"You can also associate records with","depth":10,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"labels","depth":10,"on_screen":false,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"labels","depth":11,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"for individual record pairs or multiple pairs of records in bulk. For each association, depending on your","depth":10,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"association limits","depth":10,"on_screen":false,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"association limits","depth":11,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":", you can include multiple labels.","depth":10,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"To associate two records and set a label to describe the association, make a","depth":11,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"PUT","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"request to","depth":11,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"/crm/objects/2026-03/{objectType}/{objectId}/associations/{toObjectType}/{toObjectId}","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":". In the request URL, include the","depth":11,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"id","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"values of the two records you’re associating.","depth":11,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"To bulk create labeled associations between records of the same objects, make a","depth":11,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"POST","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"request to","depth":11,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"/crm/associations/2026-03/{fromObjectType}/{toObjectType}/batch/create","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":". In the request body, include the","depth":11,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"id","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"values of records to associate in addition to the required parameters below.","depth":11,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"In the request body, include the following information to indicate the labeled association you want to create:","depth":10,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"associationCategory","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":": either","depth":11,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"HUBSPOT_DEFINED","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"(default label) or","depth":11,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"USER_DEFINED","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"(custom label).","depth":11,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"associationTypeId","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":": the numerical ID value for the label. If using a default label (e.g., Primary company), refer to this","depth":11,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"list of default type IDs.","depth":11,"on_screen":false,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"list of default type IDs.","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"If you’re using a custom label, you’ll need to","depth":11,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"retrieve the labels","depth":11,"on_screen":false,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"retrieve the labels","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"between those objects.","depth":11,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"For cross-object and paired label relationships, ensure you use the","depth":11,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"typeId","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"that refers to the correct direction. For example, if your","depth":11,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"fromObjectType","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"is","depth":11,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"contact","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"and your","depth":11,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"toObjectType","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"is","depth":11,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"}]...
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Platform Sprint 3 Q2 - Platform Team - Scrum Board Platform Sprint 3 Q2 - Platform Team - Scrum Board - Jira
Platform Sprint 3 Q2 - Platform Team - Scrum Board - Jira
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
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Service-Desk - Queues - Platform team - Service space - Jira
Jy 20807 check various issues with stages by nikolaybiaivanov · Pull Request #12041 · jiminny/app
Jy 20807 check various issues with stages by nikolaybiaivanov · Pull Request #12041 · jiminny/app
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Userpilot | Ask Jiminny Report Generated
Userpilot | Ask Jiminny Report Generated
JY-20773 fix user pilot tracking ofr automated report generated by LakyLak · Pull Request #12024 · jiminny/app
JY-20773 fix user pilot tracking ofr automated report generated by LakyLak · Pull Request #12024 · jiminny/app
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Associate records - HubSpot docs
Associate records - HubSpot docs
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Associate records
API Guide
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POST Report high usage
POST
Report high usage
PUT Associate records (labeled)
PUT
Associate records (labeled)
PUT Associate records (default)
PUT
Associate records (default)
DEL Remove association between records
DEL
Remove association between records
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Associations schema
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Exports
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On this page
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Associate records
Associate records
Associate records without a label
Associate records without a label
Associate records with a label
Associate records with a label
Retrieve associated records
Retrieve associated records
Update record association labels
Update record association labels
Remove record associations
Remove record associations
Remove all associations between records
Remove all associations between records
Remove associations with specific labels
Remove associations with specific labels
Report on high association usage
Report on high association usage
Limits
Limits
Association type ID values
Association type ID values
Company to object
Company to object
Contact to object
Contact to object
Deal to object
Deal to object
Ticket to object
Ticket to object
Lead to object
Lead to object
Appointment to object
Appointment to object
Goal to object
Goal to object
Course to object
Course to object
Listing to object
Listing to object
Service to object
Service to object
Call to object
Call to object
Email to object
Email to object
Feedback submission to object
Feedback submission to object
Meeting to object
Meeting to object
Note to object
Note to object
Postal mail to object
Postal mail to object
Task to object
Task to object
Communication (SMS, WhatsApp, or LinkedIn message) to object
Communication (SMS, WhatsApp, or LinkedIn message) to object
Invoice to object
Invoice to object
Payment to object
Payment to object
Payment link to object
Payment link to object
Quote to object
Quote to object
Line item to object
Line item to object
Order to object
Order to object
Cart to object
Cart to object
Discount to object
Discount to object
Fee to object
Fee to object
Subscription to object
Subscription to object
Tax to object
Tax to object
Project to object
Project to object
v1 associations (legacy)
v1 associations (legacy)
Associate records
Associate records
Associate records
Use the associations API to create, manage, and remove associations between CRM records.
Documentation Index
Documentation Index
Fetch the complete documentation index at:
https://developers.hubspot.com/docs/llms.txt
https://developers.hubspot.com/docs/llms.txt
Use this file to discover all available pages before exploring further.
For the previous version, please see the documentation for the
v3 Associations API
v3 Associations API
.
Scope requirements
Scope requirements
Associations represent the relationships between objects and activities in the HubSpot CRM. Record associations can exist between records of different objects (e.g., Contact to Company), as well as within the same object (e.g., Company to Company).
The Associations API includes two sets of endpoints:
Association details endpoints
: create, edit, and remove associations between records. In this article, you’ll learn more about using the associations details endpoints.
Association schema endpoints
Association schema endpoints
: view your account’s association definitions (also known as types), create and manage custom association labels, and set limits for associations. Learn more about using the
associations schema endpoints
associations schema endpoints
.
Learn more about objects, records, properties, and associations APIs in the
Understanding the CRM
Understanding the CRM
guide.
Navigate to header Associate records
Navigate to header
Associate records
You can associate records with each other unlabeled (i.e. contact and company associated with no descriptor) or with labels (e.g., contact and company associated and the contact is the company’s
Decision maker
).
The number of associations a record can have depends on
the object and your HubSpot subscription.
the object and your HubSpot subscription.
Navigate to header Associate records without a label
Navigate to header
Associate records without a label
You can create a default unlabeled association between two records, or set up unlabeled associations for records in bulk. To set up an individual default association between two records, make a
PUT
request to
/crm/objects/2026-03/{fromObjectType}/{fromObjectId}/associations/default/{toObjectType}/{toObjectId}
In the request URL, include:
fromObjectType
:
the ID of the object you’re associating. To find the ID values, refer to this
list of object type IDs,
list of object type IDs,
or for contacts, companies, deals, tickets, and notes, you can use the object name (e.g.,
contact
,
company
).
fromObjectId
:
the ID of the record to associate.
toObjectType
:
the ID of the object you’re associating the record to. To find the ID values, refer to this
list of object type IDs,
list of object type IDs,
or for contacts, companies, deals, tickets, and notes, you can use the object name (e.g.,
contact
,
company
).
toObjectId
:
the ID of the record to associate to.
For example, to associate a contact record whose ID is
12345
with a company record whose ID is
67891
, your request URL would be:
/crm/objects/2026-03/contact/12345/associations/default/company/67891
.
To associate records without a label in bulk, make a
POST
request to
/crm/associations/2026-03/{fromObjectType}/{toObjectType}/batch/associate/default
. In the request body, include
objectId
values for the records you want to associate. For example:
Report incorrect code
Copy the contents from the code block
Ask AI
{
"inputs"
: [
{
"from"
: {
"id"
:
"12345"
},
"to"
: {
"id"
:
"56678"
}
},
{
"from"
: {
"id"
:
"49475850"
},
"to"
: {
"id"
:
"2049585"
}
},
{
"from"
: {
"id"
:
"304958"
},
"to"
: {
"id"
:
"204959"
}
}
]
}
Navigate to header Associate records with a label
Navigate to header
Associate records with a label
You can also associate records with
labels
labels
for individual record pairs or multiple pairs of records in bulk. For each association, depending on your
association limits
association limits
, you can include multiple labels.
To associate two records and set a label to describe the association, make a
PUT
request to
/crm/objects/2026-03/{objectType}/{objectId}/associations/{toObjectType}/{toObjectId}
. In the request URL, include the
id
values of the two records you’re associating.
To bulk create labeled associations between records of the same objects, make a
POST
request to
/crm/associations/2026-03/{fromObjectType}/{toObjectType}/batch/create
. In the request body, include the
id
values of records to associate in addition to the required parameters below.
In the request body, include the following information to indicate the labeled association you want to create:
associationCategory
: either
HUBSPOT_DEFINED
(default label) or
USER_DEFINED
(custom label).
associationTypeId
: the numerical ID value for the label. If using a default label (e.g., Primary company), refer to this
list of default type IDs.
list of default type IDs.
If you’re using a custom label, you’ll need to
retrieve the labels
retrieve the labels
between those objects.
For cross-object and paired label relationships, ensure you use the
typeId
that refers to the correct direction. For example, if your
fromObjectType
is
contact
and your
toObjectType
is...
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Associate records - HubSpot docs — Work
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Platform Sprint 3 Q2 - Platform Team - Scrum Board Platform Sprint 3 Q2 - Platform Team - Scrum Board - Jira
Platform Sprint 3 Q2 - Platform Team - Scrum Board - Jira
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
Service-Desk - Queues - Platform team - Service space - Jira
Service-Desk - Queues - Platform team - Service space - Jira
Jy 20807 check various issues with stages by nikolaybiaivanov · Pull Request #12041 · jiminny/app
Jy 20807 check various issues with stages by nikolaybiaivanov · Pull Request #12041 · jiminny/app
Sentry
Sentry
Pull requests · jiminny/app
Pull requests · jiminny/app
Userpilot | Ask Jiminny Report Generated
Userpilot | Ask Jiminny Report Generated
JY-20773 fix user pilot tracking ofr automated report generated by LakyLak · Pull Request #12024 · jiminny/app
JY-20773 fix user pilot tracking ofr automated report generated by LakyLak · Pull Request #12024 · jiminny/app
Jiminny
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Associate records - HubSpot docs
Associate records - HubSpot docs
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Associate records
API Guide
API Guide
POST Report high usage
POST
Report high usage
PUT Associate records (labeled)
PUT
Associate records (labeled)
PUT Associate records (default)
PUT
Associate records (default)
DEL Remove association between records
DEL
Remove association between records
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Associations schema
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Associate records
Associate records
Associate records without a label
Associate records without a label
Associate records with a label
Associate records with a label
Retrieve associated records
Retrieve associated records
Update record association labels
Update record association labels
Remove record associations
Remove record associations
Remove all associations between records
Remove all associations between records
Remove associations with specific labels
Remove associations with specific labels
Report on high association usage
Report on high association usage
Limits
Limits
Association type ID values
Association type ID values
Company to object
Company to object
Contact to object
Contact to object
Deal to object
Deal to object
Ticket to object
Ticket to object
Lead to object
Lead to object
Appointment to object
Appointment to object
Goal to object
Goal to object
Course to object
Course to object
Listing to object
Listing to object
Service to object
Service to object
Call to object
Call to object
Email to object
Email to object
Feedback submission to object
Feedback submission to object
Meeting to object
Meeting to object
Note to object
Note to object
Postal mail to object
Postal mail to object
Task to object
Task to object
Communication (SMS, WhatsApp, or LinkedIn message) to object
Communication (SMS, WhatsApp, or LinkedIn message) to object
Invoice to object
Invoice to object
Payment to object
Payment to object
Payment link to object
Payment link to object
Quote to object
Quote to object
Line item to object
Line item to object
Order to object
Order to object
Cart to object
Cart to object
Discount to object
Discount to object
Fee to object
Fee to object
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Subscription to object
Tax to object
Tax to object
Project to object
Project to object
v1 associations (legacy)
v1 associations (legacy)
Associate records
Associate records
Associate records
Use the associations API to create, manage, and remove associations between CRM records.
Documentation Index
Documentation Index
Fetch the complete documentation index at:
https://developers.hubspot.com/docs/llms.txt
https://developers.hubspot.com/docs/llms.txt
Use this file to discover all available pages before exploring further.
For the previous version, please see the documentation for the
v3 Associations API
v3 Associations API
.
Scope requirements
Scope requirements
Associations represent the relationships between objects and activities in the HubSpot CRM. Record associations can exist between records of different objects (e.g., Contact to Company), as well as within the same object (e.g., Company to Company).
The Associations API includes two sets of endpoints:
Association details endpoints
: create, edit, and remove associations between records. In this article, you’ll learn more about using the associations details endpoints.
Association schema endpoints
Association schema endpoints
: view your account’s association definitions (also known as types), create and manage custom association labels, and set limits for associations. Learn more about using the
associations schema endpoints
associations schema endpoints
.
Learn more about objects, records, properties, and associations APIs in the
Understanding the CRM
Understanding the CRM
guide.
Navigate to header Associate records
Navigate to header
Associate records
You can associate records with each other unlabeled (i.e. contact and company associated with no descriptor) or with labels (e.g., contact and company associated and the contact is the company’s
Decision maker
).
The number of associations a record can have depends on
the object and your HubSpot subscription.
the object and your HubSpot subscription.
Navigate to header Associate records without a label
Navigate to header
Associate records without a label
You can create a default unlabeled association between two records, or set up unlabeled associations for records in bulk. To set up an individual default association between two records, make a
PUT
request to
/crm/objects/2026-03/{fromObjectType}/{fromObjectId}/associations/default/{toObjectType}/{toObjectId}
In the request URL, include:
fromObjectType
:
the ID of the object you’re associating. To find the ID values, refer to this
list of object type IDs,
list of object type IDs,
or for contacts, companies, deals, tickets, and notes, you can use the object name (e.g.,
contact
,
company
).
fromObjectId
:
the ID of the record to associate.
toObjectType
:
the ID of the object you’re associating the record to. To find the ID values, refer to this
list of object type IDs,
list of object type IDs,
or for contacts, companies, deals, tickets, and notes, you can use the object name (e.g.,
contact
,
company
).
toObjectId
:
the ID of the record to associate to.
For example, to associate a contact record whose ID is
12345
with a company record whose ID is
67891
, your request URL would be:
/crm/objects/2026-03/contact/12345/associations/default/company/67891
.
To associate records without a label in bulk, make a
POST
request to
/crm/associations/2026-03/{fromObjectType}/{toObjectType}/batch/associate/default
. In the request body, include
objectId
values for the records you want to associate. For example:
Report incorrect code
Copy the contents from the code block
Ask AI
{
"inputs"
: [
{
"from"
: {
"id"
:
"12345"
},
"to"
: {
"id"
:
"56678"
}
},
{
"from"
: {
"id"
:
"49475850"
},
"to"
: {
"id"
:
"2049585"
}
},
{
"from"
: {
"id"
:
"304958"
},
"to"
: {
"id"
:
"204959"
}
}
]
}
Navigate to header Associate records with a label
Navigate to header
Associate records with a label
You can also associate records with
labels
labels
for individual record pairs or multiple pairs of records in bulk. For each association, depending on your
association limits
association limits
, you can include multiple labels.
To associate two records and set a label to describe the association, make a
PUT
request to
/crm/objects/2026-03/{objectType}/{objectId}/associations/{toObjectType}/{toObjectId}
. In the request URL, include the
id
values of the two records you’re associating.
To bulk create labeled associations between records of the same objects, make a
POST
request to
/crm/associations/2026-03/{fromObjectType}/{toObjectType}/batch/create
. In the request body, include the
id
values of records to associate in addition to the required parameters below.
In the request body, include the following information to indicate the labeled association you want to create:
associationCategory
: either
HUBSPOT_DEFINED
(default label) or
USER_DEFINED...
|
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Learn more about using the","depth":11,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"associations schema endpoints","depth":11,"on_screen":false,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"associations schema endpoints","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":".","depth":11,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Learn more about objects, records, properties, and associations APIs in the","depth":10,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"Understanding the CRM","depth":10,"on_screen":false,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"Understanding the CRM","depth":11,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"guide.","depth":10,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXHeading","text":"Navigate to header Associate records","depth":9,"on_screen":false,"help_text":"","role_description":"heading","subrole":"AXUnknown"},{"role":"AXLink","text":"Navigate to header","depth":11,"on_screen":false,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Associate records","depth":11,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"You can associate records with each other unlabeled (i.e. contact and company associated with no descriptor) or with labels (e.g., contact and company associated and the contact is the company’s","depth":10,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Decision maker","depth":11,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":").","depth":10,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"The number of associations a record can have depends on","depth":11,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"the object and your HubSpot subscription.","depth":11,"on_screen":false,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"the object and your HubSpot subscription.","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXHeading","text":"Navigate to header Associate records without a label","depth":9,"on_screen":false,"help_text":"","role_description":"heading","subrole":"AXUnknown"},{"role":"AXLink","text":"Navigate to header","depth":11,"on_screen":false,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Associate records without a label","depth":11,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"You can create a default unlabeled association between two records, or set up unlabeled associations for records in bulk. To set up an individual default association between two records, make a","depth":10,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"PUT","depth":11,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"request to","depth":10,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"/crm/objects/2026-03/{fromObjectType}/{fromObjectId}/associations/default/{toObjectType}/{toObjectId}","depth":11,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"In the request URL, include:","depth":10,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"fromObjectType","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":":","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"the ID of the object you’re associating. To find the ID values, refer to this","depth":11,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"list of object type IDs,","depth":11,"on_screen":false,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"list of object type IDs,","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"or for contacts, companies, deals, tickets, and notes, you can use the object name (e.g.,","depth":11,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"contact","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":",","depth":11,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"company","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":").","depth":11,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"fromObjectId","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":":","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"the ID of the record to associate.","depth":11,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"toObjectType","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":":","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"the ID of the object you’re associating the record to. To find the ID values, refer to this","depth":11,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"list of object type IDs,","depth":11,"on_screen":false,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"list of object type IDs,","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"or for contacts, companies, deals, tickets, and notes, you can use the object name (e.g.,","depth":11,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"contact","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":",","depth":11,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"company","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":").","depth":11,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"toObjectId","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":":","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"the ID of the record to associate to.","depth":11,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"For example, to associate a contact record whose ID is","depth":10,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"12345","depth":11,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"with a company record whose ID is","depth":10,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"67891","depth":11,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":", your request URL would be:","depth":10,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"/crm/objects/2026-03/contact/12345/associations/default/company/67891","depth":11,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":".","depth":10,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"To associate records without a label in bulk, make a","depth":10,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"POST","depth":11,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"request to","depth":10,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"/crm/associations/2026-03/{fromObjectType}/{toObjectType}/batch/associate/default","depth":11,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":". In the request body, include","depth":10,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"objectId","depth":11,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"values for the records you want to associate. For example:","depth":10,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXButton","text":"Report incorrect code","depth":10,"on_screen":false,"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXButton","text":"Copy the contents from the code block","depth":10,"on_screen":false,"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXButton","text":"Ask AI","depth":10,"on_screen":false,"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"{","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"inputs\"","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":": [","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"{","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"from\"","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":": 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{","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"id\"","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":":","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"49475850\"","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"},","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"to\"","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":": {","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"id\"","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":":","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"2049585\"","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"}","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"},","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"{","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"from\"","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":": {","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"id\"","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":":","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"304958\"","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"},","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"to\"","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":": {","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"id\"","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":":","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"\"204959\"","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"}","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"}","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"]","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"}","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXHeading","text":"Navigate to header Associate records with a label","depth":9,"on_screen":false,"help_text":"","role_description":"heading","subrole":"AXUnknown"},{"role":"AXLink","text":"Navigate to header","depth":11,"on_screen":false,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Associate records with a label","depth":11,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"You can also associate records with","depth":10,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"labels","depth":10,"on_screen":false,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"labels","depth":11,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"for individual record pairs or multiple pairs of records in bulk. For each association, depending on your","depth":10,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"association limits","depth":10,"on_screen":false,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"association limits","depth":11,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":", you can include multiple labels.","depth":10,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"To associate two records and set a label to describe the association, make a","depth":11,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"PUT","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"request to","depth":11,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"/crm/objects/2026-03/{objectType}/{objectId}/associations/{toObjectType}/{toObjectId}","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":". In the request URL, include the","depth":11,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"id","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"values of the two records you’re associating.","depth":11,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"To bulk create labeled associations between records of the same objects, make a","depth":11,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"POST","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"request to","depth":11,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"/crm/associations/2026-03/{fromObjectType}/{toObjectType}/batch/create","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":". In the request body, include the","depth":11,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"id","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"values of records to associate in addition to the required parameters below.","depth":11,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"In the request body, include the following information to indicate the labeled association you want to create:","depth":10,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"associationCategory","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":": either","depth":11,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"HUBSPOT_DEFINED","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"(default label) or","depth":11,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"USER_DEFINED","depth":12,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"}]...
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Platform Sprint 3 Q2 - Platform Team - Scrum Board Platform Sprint 3 Q2 - Platform Team - Scrum Board - Jira
Platform Sprint 3 Q2 - Platform Team - Scrum Board - Jira
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
Service-Desk - Queues - Platform team - Service space - Jira
Service-Desk - Queues - Platform team - Service space - Jira
Jy 20807 check various issues with stages by nikolaybiaivanov · Pull Request #12041 · jiminny/app
Jy 20807 check various issues with stages by nikolaybiaivanov · Pull Request #12041 · jiminny/app
Sentry
Sentry
Pull requests · jiminny/app
Pull requests · jiminny/app
Userpilot | Ask Jiminny Report Generated
Userpilot | Ask Jiminny Report Generated
JY-20773 fix user pilot tracking ofr automated report generated by LakyLak · Pull Request #12024 · jiminny/app
JY-20773 fix user pilot tracking ofr automated report generated by LakyLak · Pull Request #12024 · jiminny/app
Jiminny
Jiminny
Associate records - HubSpot docs
Associate records - HubSpot docs
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POST Report high usage
POST
Report high usage
PUT Associate records (labeled)
PUT
Associate records (labeled)
PUT Associate records (default)
PUT
Associate records (default)
DEL Remove association between records
DEL
Remove association between records
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On this page
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Associate records
Associate records
Associate records without a label
Associate records without a label
Associate records with a label
Associate records with a label
Retrieve associated records
Retrieve associated records
Update record association labels
Update record association labels
Remove record associations
Remove record associations
Remove all associations between records
Remove all associations between records
Remove associations with specific labels
Remove associations with specific labels
Report on high association usage
Report on high association usage
Limits
Limits
Association type ID values
Association type ID values
Company to object
Company to object
Contact to object
Contact to object
Deal to object
Deal to object
Ticket to object
Ticket to object
Lead to object
Lead to object
Appointment to object
Appointment to object
Goal to object
Goal to object
Course to object
Course to object
Listing to object
Listing to object
Service to object
Service to object
Call to object
Call to object
Email to object
Email to object
Feedback submission to object
Feedback submission to object
Meeting to object
Meeting to object
Note to object
Note to object
Postal mail to object
Postal mail to object
Task to object
Task to object
Communication (SMS, WhatsApp, or LinkedIn message) to object
Communication (SMS, WhatsApp, or LinkedIn message) to object
Invoice to object
Invoice to object
Payment to object
Payment to object
Payment link to object
Payment link to object
Quote to object
Quote to object
Line item to object
Line item to object
Order to object
Order to object
Cart to object
Cart to object
Discount to object
Discount to object
Fee to object
Fee to object
Subscription to object
Subscription to object
Tax to object
Tax to object
Project to object
Project to object
v1 associations (legacy)
v1 associations (legacy)
Associate records
Associate records
Associate records
Use the associations API to create, manage, and remove associations between CRM records.
Documentation Index
Documentation Index
Fetch the complete documentation index at:
https://developers.hubspot.com/docs/llms.txt
https://developers.hubspot.com/docs/llms.txt
Use this file to discover all available pages before exploring further.
For the previous version, please see the documentation for the
v3 Associations API
v3 Associations API
.
Scope requirements
Scope requirements
Associations represent the relationships between objects and activities in the HubSpot CRM. Record associations can exist between records of different objects (e.g., Contact to Company), as well as within the same object (e.g., Company to Company).
The Associations API includes two sets of endpoints:
Association details endpoints
: create, edit, and remove associations between records. In this article, you’ll learn more about using the associations details endpoints.
Association schema endpoints
Association schema endpoints
: view your account’s association definitions (also known as types), create and manage custom association labels, and set limits for associations. Learn more about using the
associations schema endpoints
associations schema endpoints
.
Learn more about objects, records, properties, and associations APIs in the
Understanding the CRM
Understanding the CRM
guide.
Navigate to header Associate records
Navigate to header
Associate records
You can associate records with each other unlabeled (i.e. contact and company associated with no descriptor) or with labels (e.g., contact and company associated and the contact is the company’s
Decision maker
).
The number of associations a record can have depends on
the object and your HubSpot subscription.
the object and your HubSpot subscription.
Navigate to header Associate records without a label
Navigate to header
Associate records without a label
You can create a default unlabeled association between two records, or set up unlabeled associations for records in bulk. To set up an individual default association between two records, make a
PUT
request to
/crm/objects/2026-03/{fromObjectType}/{fromObjectId}/associations/default/{toObjectType}/{toObjectId}
In the request URL, include:
fromObjectType
:
the ID of the object you’re associating. To find the ID values, refer to this
list of object type IDs,
list of object type IDs,
or for contacts, companies, deals, tickets, and notes, you can use the object name (e.g.,
contact
,
company
).
fromObjectId
:
the ID of the record to associate.
toObjectType
:
the ID of the object you’re associating the record to. To find the ID values, refer to this
list of object type IDs,
list of object type IDs,
or for contacts, companies, deals, tickets, and notes, you can use the object name (e.g.,
contact
,
company
).
toObjectId
:
the ID of the record to associate to.
For example, to associate a contact record whose ID is
12345
with a company record whose ID is
67891
, your request URL would be:
/crm/objects/2026-03/contact/12345/associations/default/company/67891
.
To associate records without a label in bulk, make a
POST
request to
/crm/associations/2026-03/{fromObjectType}/{toObjectType}/batch/associate/default
. In the request body, include
objectId
values for the records you want to associate. For example:
Report incorrect code
Copy the contents from the code block
Ask AI
{
"inputs"
: [
{
"from"
: {
"id"
:
"12345"
},
"to"
: {
"id"
:
"56678"
}
},
{
"from"
: {
"id"
:
"49475850"
},
"to"
: {
"id"
:
"2049585"
}
},
{
"from"
: {
"id"
:
"304958"
},
"to"
: {
"id"
:
"204959"
}
}
]
}
Navigate to header Associate records with a label
Navigate to header
Associate records with a label
You can also associate records with
labels
labels
for individual record pairs or multiple pairs of records in bulk. For each association, depending on your
association limits
association limits
, you can include multiple labels.
To associate two records and set a label to describe the association, make a
PUT
request to
/crm/objects/2026-03/{objectType}/{objectId}/associations/{toObjectType}/{toObjectId}
. In the request URL, include the
id
values of the two records you’re associating.
To bulk create labeled associations between records of the same objects, make a
POST
request to
/crm/associations/2026-03/{fromObjectType}/{toObjectType}/batch/create
. In the request body, include the
id
values of records to associate in addition to the required parameters below.
In the request body, include the following information to indicate the labeled association you want to create:
associationCategory
: either
HUBSPOT_DEFINED
(default label) or
USER_DEFINED...
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Associate records - HubSpot docs — Work
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Platform Sprint 3 Q2 - Platform Team - Scrum Board Platform Sprint 3 Q2 - Platform Team - Scrum Board - Jira
Platform Sprint 3 Q2 - Platform Team - Scrum Board - Jira
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
Service-Desk - Queues - Platform team - Service space - Jira
Service-Desk - Queues - Platform team - Service space - Jira
Jy 20807 check various issues with stages by nikolaybiaivanov · Pull Request #12041 · jiminny/app
Jy 20807 check various issues with stages by nikolaybiaivanov · Pull Request #12041 · jiminny/app
Sentry
Sentry
Pull requests · jiminny/app
Pull requests · jiminny/app
Userpilot | Ask Jiminny Report Generated
Userpilot | Ask Jiminny Report Generated
JY-20773 fix user pilot tracking ofr automated report generated by LakyLak · Pull Request #12024 · jiminny/app
JY-20773 fix user pilot tracking ofr automated report generated by LakyLak · Pull Request #12024 · jiminny/app
Jiminny
Jiminny
Associate records - HubSpot docs
Associate records - HubSpot docs
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POST Report high usage
POST
Report high usage
PUT Associate records (labeled)
PUT
Associate records (labeled)
PUT Associate records (default)
PUT
Associate records (default)
DEL Remove association between records
DEL
Remove association between records
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On this page
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Associate records
Associate records
Associate records without a label
Associate records without a label
Associate records with a label
Associate records with a label
Retrieve associated records
Retrieve associated records
Update record association labels
Update record association labels
Remove record associations
Remove record associations
Remove all associations between records
Remove all associations between records
Remove associations with specific labels
Remove associations with specific labels
Report on high association usage
Report on high association usage
Limits
Limits
Association type ID values
Association type ID values
Company to object
Company to object
Contact to object
Contact to object
Deal to object
Deal to object
Ticket to object
Ticket to object
Lead to object
Lead to object
Appointment to object
Appointment to object
Goal to object
Goal to object
Course to object
Course to object
Listing to object
Listing to object
Service to object
Service to object
Call to object
Call to object
Email to object
Email to object
Feedback submission to object
Feedback submission to object
Meeting to object
Meeting to object
Note to object
Note to object
Postal mail to object
Postal mail to object
Task to object
Task to object
Communication (SMS, WhatsApp, or LinkedIn message) to object
Communication (SMS, WhatsApp, or LinkedIn message) to object
Invoice to object
Invoice to object
Payment to object
Payment to object
Payment link to object
Payment link to object
Quote to object
Quote to object
Line item to object
Line item to object
Order to object
Order to object
Cart to object
Cart to object
Discount to object
Discount to object
Fee to object
Fee to object
Subscription to object
Subscription to object
Tax to object
Tax to object
Project to object
Project to object
v1 associations (legacy)
v1 associations (legacy)
Associate records
Associate records
Associate records
Use the associations API to create, manage, and remove associations between CRM records.
Documentation Index
Documentation Index
Fetch the complete documentation index at:
https://developers.hubspot.com/docs/llms.txt
https://developers.hubspot.com/docs/llms.txt
Use this file to discover all available pages before exploring further.
For the previous version, please see the documentation for the
v3 Associations API
v3 Associations API
.
Scope requirements
Scope requirements
Associations represent the relationships between objects and activities in the HubSpot CRM. Record associations can exist between records of different objects (e.g., Contact to Company), as well as within the same object (e.g., Company to Company).
The Associations API includes two sets of endpoints:
Association details endpoints
: create, edit, and remove associations between records. In this article, you’ll learn more about using the associations details endpoints.
Association schema endpoints
Association schema endpoints
: view your account’s association definitions (also known as types), create and manage custom association labels, and set limits for associations. Learn more about using the
associations schema endpoints
associations schema endpoints
.
Learn more about objects, records, properties, and associations APIs in the
Understanding the CRM
Understanding the CRM
guide.
Navigate to header Associate records
Navigate to header
Associate records
You can associate records with each other unlabeled (i.e. contact and company associated with no descriptor) or with labels (e.g., contact and company associated and the contact is the company’s
Decision maker...
|
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Platform Sprint 3 Q2 - Platform Team - Scrum Board Platform Sprint 3 Q2 - Platform Team - Scrum Board - Jira
Platform Sprint 3 Q2 - Platform Team - Scrum Board - Jira
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
SevenShores\Hubspot\Exceptions\BadRequest: Client error: `POST https://api.hubapi.com/crm/v3/objects/contact/search` resulted in a `429 Too Many Requests` response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT
Service-Desk - Queues - Platform team - Service space - Jira
Service-Desk - Queues - Platform team - Service space - Jira
Jy 20807 check various issues with stages by nikolaybiaivanov · Pull Request #12041 · jiminny/app
Jy 20807 check various issues with stages by nikolaybiaivanov · Pull Request #12041 · jiminny/app
Sentry
Sentry
Pull requests · jiminny/app
Pull requests · jiminny/app
Userpilot | Ask Jiminny Report Generated
Userpilot | Ask Jiminny Report Generated
JY-20773 fix user pilot tracking ofr automated report generated by LakyLak · Pull Request #12024 · jiminny/app
JY-20773 fix user pilot tracking ofr automated report generated by LakyLak · Pull Request #12024 · jiminny/app
Jiminny
Jiminny
Associate records - HubSpot docs
Associate records - HubSpot docs
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POST Report high usage
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Associate records
Associate records
Associate records without a label
Associate records without a label
Associate records with a label
Associate records with a label
Retrieve associated records
Retrieve associated records
Update record association labels
Update record association labels
Remove record associations
Remove record associations
Remove all associations between records
Remove all associations between records
Remove associations with specific labels
Remove associations with specific labels
Report on high association usage
Report on high association usage
Limits
Limits
Association type ID values
Association type ID values
Company to object
Company to object
Contact to object
Contact to object
Deal to object
Deal to object
Ticket to object
Ticket to object
Lead to object
Lead to object
Appointment to object
Appointment to object
Goal to object
Goal to object
Course to object
Course to object
Listing to object
Listing to object
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Service to object
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Call to object
Email to object
Email to object
Feedback submission to object
Feedback submission to object
Meeting to object
Meeting to object
Note to object
Note to object
Postal mail to object
Postal mail to object
Task to object
Task to object
Communication (SMS, WhatsApp, or LinkedIn message) to object
Communication (SMS, WhatsApp, or LinkedIn message) to object
Invoice to object
Invoice to object
Payment to object
Payment to object
Payment link to object
Payment link to object
Quote to object
Quote to object
Line item to object
Line item to object
Order to object
Order to object
Cart to object
Cart to object
Discount to object
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Subscription to object
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Tax to object
Project to object
Project to object
v1 associations (legacy)
v1 associations (legacy)
Associate records
Associate records
Associate records
Use the associations API to create, manage, and remove associations between CRM records.
Documentation Index
Documentation Index
Fetch the complete documentation index at:
https://developers.hubspot.com/docs/llms.txt
https://developers.hubspot.com/docs/llms.txt
Use this file to discover all available pages before exploring further.
For the previous version, please see the documentation for the
v3 Associations API
v3 Associations API
.
Scope requirements
Scope requirements
Associations represent the relationships between objects and activities in the HubSpot CRM. Record associations can exist between records of different objects (e.g., Contact to Company), as well as within the same object (e.g., Company to Company).
The Associations API includes two sets of endpoints:
Association details endpoints
: create, edit, and remove associations between records. In this article, you’ll learn more about using the associations details endpoints.
Association schema endpoints
Association schema endpoints
: view your account’s association definitions (also known as types), create and manage custom association labels, and set limits for associations. Learn more about using the
associations schema endpoints
associations schema endpoints
.
Learn more about objects, records, properties, and associations APIs in the
Understanding the CRM
Understanding the CRM
guide.
Navigate to header Associate records
Navigate to header
Associate records
You can associate records with each other unlabeled (i.e. contact and company associated with no descriptor) or with labels (e.g., contact and company associated and the contact is the company’s
Decision maker...
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