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iTerm2ShellEditViewSessionScriptsProfilesWindowHel iTerm2ShellEditViewSessionScriptsProfilesWindowHelp(ah]= Support Daily - in 30 ml100%DEV (docker)Thu 7 May 14:30:18181₴6DOCKERLast login: Thu MayDEV (docker)H82APP (-zsh)-zsh• 84screenpipe*•$5-zsh7 09:44:56onttys006Poetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentsPoetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentslukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-20773-fix-automated-reports-user-pilot-tracking) $ devroot@docker_lamp_1:/home/jiminny# php artisan crm:sync-opportunity --teamId=2 --from='2026-05-01 00:00:00*Syncing opportunity for HubspotYour HubSpotaccount has become disconnected.Please login to Jiminny to reconnect. skipping...root@docker_lamp_1:/home/jiminny# php artisan jiminny:token-info -A 1499 -RDEVaccess_tokenCFPAsBNZxoDp5kAcRyeB1QoE5SM7DSgNuYTFSAFoAYABo3tj9DHAAeAACNeR-JHgMxIZQLNQMl8kQEwrAgwACAkUAhIJBB4BAQEDBxiCiYwCIN7Y_Qwo0qwCMhTnG549n-YtNuc1jgj-2AsLPSmw3DoyQLNQM18kQEwrAiUACBkGawEFThwBARIBAQEEATEEAQEBAQEBAQEBAQUBEggBAQEBAY1access_token_expires_at=> 2026-05-07 11:41:20riPS-31Claude...
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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
Show more
10:42
Retry
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Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
Copy
Give positive feedback
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
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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) │...
|
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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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Retry
You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
Retry
Edit
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Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
Copy to clipboard
php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.
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You said: Ok, so what will happen each time I make a request?
You said: Ok, so what will happen each time I make a request?
Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?
13:44
Retry
Edit
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Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Analyzed per-request overhead concerns and quantified Redis performance impact
Analyzed per-request overhead concerns and quantified Redis performance impact
Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out. But the overhead is much smaller than it sounds, and importantly, it's nowhere near your bottleneck.
What "two buckets per request" actually costs
What "two buckets per request" actually costs
Each request makes
one round trip to Redis
— not two — because you fold both bucket checks into a single Lua script that runs atomically server-side:
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
(...
|
Claude
|
Claude
|
NULL
|
2602
|
|
2603
|
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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
Show more
10:42
Retry
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Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
Copy
Give positive feedback
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...
|
Claude
|
Claude
|
NULL
|
2603
|
|
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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
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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
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...
|
Claude
|
Claude
|
NULL
|
2604
|
|
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|
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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
Show more
10:42
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Edit
Copy
Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
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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:
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lua...
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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
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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
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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...
|
Claude
|
Claude
|
NULL
|
2606
|
|
2607
|
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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
Show more
10:42
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Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
Copy
Give positive feedback
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Retry
You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
Retry
Edit
Copy
Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
Copy to clipboard
php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.
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You said: Ok, so what will happen each time I make a request?
You said: Ok, so what will happen each time I make a request?
Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?
13:44
Retry
Edit
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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'
,...
|
Claude
|
Claude
|
NULL
|
2607
|
|
2608
|
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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.
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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...
|
Claude
|
Claude
|
NULL
|
2608
|
|
2609
|
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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
]
}...
|
Claude
|
Claude
|
NULL
|
2609
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|
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|
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HubSpot rate limit implementation strategy, rename chat
HubSpot rate limit implementation strategy
More options for HubSpot rate limit implementation strategy
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
]...
|
Claude
|
Claude
|
NULL
|
2610
|
|
2611
|
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HubSpot rate limit handling with executeRequest
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HubSpot rate limit implementation strategy, rename chat
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Claude is responding
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
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...
|
Claude
|
Claude
|
NULL
|
2611
|
|
2612
|
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HubSpot rate limit implementation strategy, rename chat
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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.
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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
==...
|
Claude
|
Claude
|
NULL
|
2612
|
|
2613
|
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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'
,
-
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...
|
Claude
|
Claude
|
NULL
|
2613
|
|
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HubSpot rate limit implementation strategy
More options for HubSpot rate limit implementation strategy
Close
Claude is responding
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
Show more
10:42
Edit
Copy
Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
Copy
Give positive feedback
Give negative feedback
You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
Edit
Copy
Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
Copy to clipboard
php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.
Copy
Give positive feedback
Give negative feedback
You said: Ok, so what will happen each time I make a request?
You said: Ok, so what will happen each time I make a request?
Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?
13:44
Edit
Copy
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Analyzed per-request overhead concerns and quantified Redis performance impact
Analyzed per-request overhead concerns and quantified Redis performance impact
Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out. But the overhead is much smaller than it sounds, and importantly, it's nowhere near your bottleneck.
What "two buckets per request" actually costs
What "two buckets per request" actually costs
Each request makes
one round trip to Redis
— not two — because you fold both bucket checks into a single Lua script that runs atomically server-side:
Copy to clipboard
lua
-- KEYS[1] = burst bucket key (or search)
-- KEYS[2] = daily bucket key
-- ARGV[1] = now (ms) ARGV[2] = window_ms
-- ARGV[3] = bucket_max ARGV[4] = daily_max
-- ARGV[5] = request_id ARGV[6] = daily_ttl
-- Trim sliding window
redis
.
call
(
'ZREMRANGEBYSCORE'
,
KEYS
[
1
]
,
0
,
ARGV
[
1
]
-
ARGV
[
2
]
)
local
burst_used
=
redis
.
call
(
'ZCARD'
,
KEYS
[
1
]
)
local
daily_used
=
tonumber
(
redis
.
call
(
'GET'
,
KEYS
[
2
]
)
or
'0'
)
if
burst_used
>=
tonumber
(
ARGV
[
3
]
)
then
-- Tell caller how long to sleep until oldest entry expires
local
oldest
=
redis
.
call
(
'ZRANGE'
,
KEYS
[
1
]
,
0
,
0
,
'WITHSCORES'
)
return
{
0
,
'BURST'
,
(
oldest
[
2
]
+
ARGV
[
2
]
)
-
ARGV
[
1
]
}
end
if
daily_used
>=
tonumber
(
ARGV
[
4
]
)
then
return
{
0
,
'DAILY'
,
-
1
}
end
redis
.
call
(
'ZADD'
,
KEYS
[
1
]
,
ARGV
[
1
]
,
ARGV
[...
|
Claude
|
Claude
|
NULL
|
2614
|
|
2615
|
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HubSpot rate limit handling with executeRequest
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HubSpot rate limit implementation strategy, rename chat
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Close
Claude is responding
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
Show more
10:42
Edit
Copy
Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
Copy
Give positive feedback
Give negative feedback
You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
Edit
Copy
Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
Copy to clipboard
php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.
Copy
Give positive feedback
Give negative feedback
You said: Ok, so what will happen each time I make a request?
You said: Ok, so what will happen each time I make a request?
Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?
13:44
Edit
Copy
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Analyzed per-request overhead concerns and quantified Redis performance impact
Analyzed per-request overhead concerns and quantified Redis performance impact
Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out. But the overhead is much smaller than it sounds, and importantly, it's nowhere near your bottleneck.
What "two buckets per request" actually costs
What "two buckets per request" actually costs
Each request makes
one round trip to Redis
— not two — because you fold both bucket checks into a single Lua script that runs atomically server-side:
Copy to clipboard
lua
-- KEYS[1] = burst bucket key (or search)
-- KEYS[2] = daily bucket key
-- ARGV[1] = now (ms) ARGV[2] = window_ms
-- ARGV[3] = bucket_max ARGV[4] = daily_max
-- ARGV[5] = request_id ARGV[6] = daily_ttl
-- Trim sliding window
redis
.
call
(
'ZREMRANGEBYSCORE'
,
KEYS
[
1
]
,
0
,
ARGV
[
1
]
-
ARGV
[
2
]
)
local
burst_used
=
redis
.
call
(
'ZCARD'
,
KEYS
[
1
]
)
local
daily_used
=
tonumber
(
redis
.
call
(
'GET'
,
KEYS
[
2
]
)
or
'0'
)
if
burst_used
>=
tonumber
(
ARGV
[
3
]
)
then
-- Tell caller how long to sleep until oldest entry expires
local
oldest
=
redis
.
call
(
'ZRANGE'
,
KEYS
[
1
]
,
0
,
0
,
'WITHSCORES'
)
return
{
0
,
'BURST'
,
(
oldest
[
2
]
+
ARGV
[
2
]
)
-
ARGV
[
1
]
}
end
if
daily_used
>=
tonumber
(
ARGV
[
4
]
)
then
return...
|
Claude
|
Claude
|
NULL
|
2615
|
|
2616
|
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Close
Claude is responding
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
Show more
10:42
Edit
Copy
Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
Copy
Give positive feedback
Give negative feedback
You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
Edit
Copy
Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions...
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Close
Claude is responding
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
Show more
10:42
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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...
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Close
Claude is responding
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
Show more
10:42
Edit
Copy
Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
Copy
Give positive feedback
Give negative feedback
You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
Edit
Copy
Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further...
|
Claude
|
Claude
|
NULL
|
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HubSpot rate limit implementation strategy, rename chat
HubSpot rate limit implementation strategy
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Close
Claude is responding
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
Show more
10:42
Edit
Copy
Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
Copy
Give positive feedback
Give negative feedback
You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
Edit
Copy
Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
Copy to clipboard
php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.
Copy
Give positive feedback
Give negative feedback
You said: Ok, so what will happen each time I make a request?
You said: Ok, so what will happen each time I make a request?
Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?
13:44
Edit
Copy
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Analyzed per-request overhead concerns and quantified Redis performance impact
Analyzed per-request overhead concerns and quantified Redis performance impact
Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out. But the overhead is much smaller than it sounds, and importantly, it's nowhere near your bottleneck.
What "two buckets per request" actually costs
What "two buckets per request" actually costs
Each request makes
one round trip to Redis
— not two — because you fold both bucket checks into a single Lua script that runs atomically server-side:
Copy to clipboard
lua
-- KEYS[1] = burst bucket key (or search)
-- KEYS[2] = daily bucket key
-- ARGV[1] = now (ms) ARGV[2] = window_ms
-- ARGV[3] = bucket_max ARGV[4] = daily_max
-- ARGV[5] = request_id ARGV[6] = daily_ttl
-- Trim sliding window
redis
.
call
(
'ZREMRANGEBYSCORE'
,
KEYS
[
1
]
,
0
,
ARGV
[
1
]
-
ARGV
[
2
]
)
local
burst_used
=
redis
.
call
(
'ZCARD'
,
KEYS
[
1
]
)
local
daily_used
=
tonumber
(
redis
.
call
(
'GET'
,
KEYS
[
2
]
)
or
'0'
)
if
burst_used
>=
tonumber
(
ARGV
[
3
]
)
then
-- Tell caller how long to sleep until oldest entry expires
local
oldest
=
redis
.
call
(
'ZRANGE'
,
KEYS
[
1
]
,
0
,
0
,
'WITHSCORES'
)
return
{
0
,
'BURST'
,
(
oldest
[
2
]
+
ARGV
[
2
]
)
-
ARGV
[
1
]
}
end
if
daily_used
>=
tonumber
(
ARGV
[
4
]
)
then
return
{
0
,
'DAILY'
,
-
1
}
end
redis
.
call
(
'ZADD'
,
KEYS
[
1
]
,
ARGV
[
1
]
,
ARGV
[...
|
Claude
|
Claude
|
NULL
|
2619
|
|
2620
|
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HubSpot rate limit implementation strategy, rename chat
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Close
Claude is responding
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
Show more
10:42
Edit
Copy
Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
Copy
Give positive feedback
Give negative feedback
You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
Edit
Copy
Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
Copy to clipboard
php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls...
|
Claude
|
Claude
|
NULL
|
2620
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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
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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
Show more
10:42
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Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
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You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
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Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
Copy to clipboard
php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')...
|
Claude
|
Claude
|
NULL
|
2622
|
|
2623
|
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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
Show more
10:42
Retry
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Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
Copy
Give positive feedback
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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You said: Ok, so what will happen each time I make a request?
You said: Ok, so what will happen each time I make a request?
Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?
13:44
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Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Analyzed per-request overhead concerns and quantified Redis performance impact
Analyzed per-request overhead concerns and quantified Redis performance impact
Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out. But the overhead is much smaller than it sounds, and importantly, it's nowhere near your bottleneck.
What "two buckets per request" actually costs
What "two buckets per request" actually costs
Each request makes
one round trip to Redis
— not two — because you fold both bucket checks into a single Lua script that runs atomically server-side:
Copy to clipboard
lua
-- KEYS[1] = burst bucket key (or search)
-- KEYS[2] = daily bucket key
-- ARGV[1] = now (ms) ARGV[2] = window_ms
-- ARGV[3] = bucket_max ARGV[4] = daily_max
-- ARGV[5] = request_id ARGV[6] = daily_ttl
-- Trim sliding window
redis
.
call
(
'ZREMRANGEBYSCORE'
,
KEYS
[
1
]
,
0
,
ARGV
[
1
]
-
ARGV
[
2
]
)
local
burst_used
=
redis
.
call
(
'ZCARD'
,
KEYS
[
1
]
)
local
daily_used
=
tonumber
(
redis
.
call
(
'GET'
,
KEYS
[
2
]
)
or
'0'
)
if
burst_used
>=
tonumber
(
ARGV
[
3
]...
|
Claude
|
Claude
|
NULL
|
2623
|
|
2624
|
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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
Show more
10:42
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Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
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Retry
You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
Retry
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
]
)...
|
Claude
|
Claude
|
NULL
|
2624
|
|
2625
|
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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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Retry
You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
Retry
Edit
Copy
Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
Copy to clipboard
php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.
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You said: Ok, so what will happen each time I make a request?
You said: Ok, so what will happen each time I make a request?
Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?
13:44
Retry
Edit
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Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Analyzed per-request overhead concerns and quantified Redis performance impact
Analyzed per-request overhead concerns and quantified Redis performance impact
Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out. But the overhead is much smaller than it sounds, and importantly, it's nowhere near your bottleneck.
What "two buckets per request" actually costs
What "two buckets per request" actually costs
Each request makes
one round trip to Redis
— not two — because you fold both bucket checks into a single Lua script that runs atomically server-side:
Copy to clipboard
lua
-- KEYS[1] = burst bucket key (or search)
-- KEYS[2] = daily bucket key
-- ARGV[1] = now (ms) ARGV[2] = window_ms
-- ARGV[3] = bucket_max ARGV[4] = daily_max
-- ARGV[5] = request_id ARGV[6] = daily_ttl
-- Trim sliding window
redis
.
call
(
'ZREMRANGEBYSCORE'
,
KEYS
[
1
]
,
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
(...
|
Claude
|
Claude
|
NULL
|
2625
|
|
2626
|
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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
Show more
10:42
Retry
Edit
Copy
Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
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You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
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Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
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....
|
Claude
|
Claude
|
NULL
|
2626
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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
Show more
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Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
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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.
Copy...
|
Claude
|
Claude
|
NULL
|
2627
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|
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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:
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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
Retry
Edit
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Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
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┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
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php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.
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You said: Ok, so what will happen each time I make a request?
You said: Ok, so what will happen each time I make a request?
Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?
13:44
Retry
Edit
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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...
|
Claude
|
Claude
|
NULL
|
2628
|
|
2629
|
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# [PASSWORD_DOTS] HS [PASSWORD_DOTS]
select * from teams where id = 2; # 2
select * from features; # 2
select * from team_features where team_id = 2; # 2
select * from crm_configurations where id = 2; # 2
select * from users where team_id = 2; #
select * from playbooks where team_id = 2; # event 38
select * from playbook_categories where playbook_id = 38; #
SELECT * FROM activities WHERE crm_configuration_id = 2 and crm_provider_id is not null order by id desc;
[URL_WITH_CREDENTIALS] CrmFieldOption array{id:string, label:string, value?:string}
*/
class Client extends BaseClient implements HubspotClientInterface
{
public const string MIN_API_VERSION = '2';
public const string BASE_URL = '[URL_WITH_CREDENTIALS] T
* @param callable(): T $apiCall
* @return T
*
* @throws RateLimitException
*/
private function executeRequest(callable $apiCall)
{
if (! $this->rateLimiter->canMakeRequest($this->config)) {
$retryAfter = $this->rateLimiter->requestAvailableIn($this->config);
$this->log->warning('[Hubspot] Rate limit exceeded, deferring request', [
'team_id' => $this->config->team_id,
'config_id' => $this->config->getId(),
'retry_after' => $retryAfter,
]);
throw new RateLimitException(
'Hubspot rate limit reached for configuration ' . $this->config->getId(),
$retryAfter,
);
}
$this->rateLimiter->incrementRequestCount($this->config);
try {
return $apiCall();
} catch (Throwable $e) {
if ($this->isHubspotRateLimit($e)) {
$retryAfter = $this->parseRetryAfter($e);
$this->log->warning('[Hubspot] Received 429 from API', [
'team_id' => $this->config->team_id,
'config_id' => $this->config->getId(),
'retry_after' => $retryAfter,
'reason' => $e->getMessage(),
]);
throw new RateLimitException('Hubspot returned 429', $retryAfter, $e);
}
throw $e;
}
}
private function isHubspotRateLimit(Throwable $e): bool
{
return method_exists($e, 'getCode') && (int) $e->getCode() === 429;
}
private function parseRetryAfter(Throwable $e): int
{
if (method_exists($e, 'getResponseHeaders')) {
$headers = $e->getResponseHeaders() ?: [];
$value = $headers['Retry-After'] ?? $headers['retry-after'] ?? null;
if (is_array($value)) {
$value = $value[0] ?? null;
}
if (is_numeric($value)) {
return (int) $value;
}
}
return 10;
}
public function getMinimumApiVersion(): string
{
return self::MIN_API_VERSION;
}
public function getInstance(): Factory
{
return new Factory([
'key' => $this->accessToken,
'oauth2' => true,
'base_url' => $this->baseUrl,
]);
}
public function getNewInstance(): Discovery
{
return \HubSpot\Factory::createWithAccessToken($this->accessToken);
}
/**
* Secondly and daily limits for Hubspot API
*
* Product Tier: Free & Starter | Professional & Enterprise | API add-on (any tier)
* Burst: 100/10 seconds | 150/10 seconds | 200/10 seconds
* Daily: 250,000 | 500,000 | 1,000,000
*
* Official documentation states: The search endpoints are rate limited to five requests per second.
* Since with 5 RPS were still hitting secondly rate limits we lowered it to 4
*/
public function getPaginatedData(array $payload, string $type, int $offset = 0): array
{
$total = 0;
$lastId = null;
$rows = [];
foreach ($this->getPaginatedDataGenerator($payload, $type, $offset, $total, $lastId) as $row) {
$rows[] = $row;
}
return ['results' => $rows, 'total' => $total, 'last_record' => $lastId];
}
/**
* @throws HubspotException
* @throws SocialAccountTokenInvalidException
* @throws BadRequest
*/
public function getPaginatedDataGenerator(
array $payload,
string $type,
int $offset = 0,
int &$total = 0,
?string &$lastRecordId = null
): \Generator {
return $this->paginationService->getPaginatedDataGenerator(
$this,
$payload,
$type,
$offset,
$total,
$lastRecordId
);
}
/**
* @throws DealApiException
* @throws CrmException
*/
public function getOpportunityById(string $crmId, array $fields): array
{
try {
$deal = $this->executeRequest(fn () => $this->getNewInstance()->crm()->deals()->basicApi()->getById(
$crmId,
implode(',', $fields),
'companies,contacts'
));
} catch (DealApiException $e) {
$this->log->info('[Hubspot] Failed to fetch opportunity', [
'crm_id' => $crmId,
'reason' => $e->getMessage(),
]);
throw $e;
}
if (! $deal instanceof DealWithAssociations) {
throw new CrmException('Deal not found');
}
return [
'id' => $deal->getId(),
'properties' => $deal->getProperties(),
'associations' => $deal->getAssociations(),
];
}
/**
* Generic batch read method for HubSpot objects
*
* @param string $objectType The object type ('deals', 'companies', 'contacts')
* @param array<string> $crmIds Array of HubSpot object IDs (max 100)
* @param array<string> $fields Array of property names to fetch
*
* @return array<string, array> Array keyed by CRM ID with object data
*/
private function batchReadObjects(string $objectType, array $crmIds, array $fields): array
{
if (empty($crmIds)) {
return [];
}
$this->validateBatchSize($objectType, $crmIds);
$this->ensureValidToken();
try {
$batchConfig = $this->createBatchConfiguration($objectType);
$batchReadRequest = $this->prepareBatchRequest($batchConfig, $crmIds, $fields);
$response = $batchConfig['api']->read($batchReadRequest);
$this->validateApiResponse($response, $objectType);
$results = $this->processApiResults($response);
$this->logBatchResults($objectType, $crmIds, $results);
return $results;
} catch (\Throwable $e) {
$this->handleBatchError($e, $objectType, $crmIds);
}
}
private function validateBatchSize(string $objectType, array $crmIds): void
{
if (count($crmIds) > 100) {
throw new \InvalidArgumentException("Batch size cannot exceed 100 {$objectType}");
}
}
private function createBatchConfiguration(string $objectType): array
{
$configurations = [
'deals' => [
'batchReadRequest' => new \HubSpot\Client\Crm\Deals\Model\BatchReadInputSimplePublicObjectId(),
'inputClass' => \HubSpot\Client\Crm\Deals\Model\SimplePublicObjectId::class,
'api' => $this->getNewInstance()->crm()->deals()->batchApi(),
],
'companies' => [
'batchReadRequest' => new \HubSpot\Client\Crm\Companies\Model\BatchReadInputSimplePublicObjectId(),
'inputClass' => \HubSpot\Client\Crm\Companies\Model\SimplePublicObjectId::class,
'api' => $this->getNewInstance()->crm()->companies()->batchApi(),
],
'contacts' => [
'batchReadRequest' => new \HubSpot\Client\Crm\Contacts\Model\BatchReadInputSimplePublicObjectId(),
'inputClass' => \HubSpot\Client\Crm\Contacts\Model\SimplePublicObjectId::class,
'api' => $this->getNewInstance()->crm()->contacts()->batchApi(),
],
];
if (! isset($configurations[$objectType])) {
throw new \InvalidArgumentException("Unsupported object type: {$objectType}");
}
return $configurations[$objectType];
}
private function prepareBatchRequest(array $batchConfig, array $crmIds, array $fields): object
{
$batchReadRequest = $batchConfig['batchReadRequest'];
$inputClass = $batchConfig['inputClass'];
$inputs = array_map(function ($crmId) use ($inputClass) {
$input = new $inputClass();
$input->setId($crmId);
return $input;
}, $crmIds);
$batchReadRequest->setInputs($inputs);
$batchReadRequest->setProperties($fields);
return $batchReadRequest;
}
private function validateApiResponse($response, string $objectType): void
{
if (! $response) {
throw new CrmException("HubSpot API returned null response for {$objectType} batch read");
}
}
private function processApiResults($response): array
{
$results = [];
$responseResults = $response->getResults();
if ($responseResults) {
foreach ($responseResults as $object) {
if ($object && $object->getId()) {
$results[$object->getId()] = [
'id' => $object->getId(),
'properties' => $object->getProperties() ?: [],
];
}
}
}
return $results;
}
private function logBatchResults(string $objectType, array $crmIds, array $results): void
{
$this->log->info("[HubSpot] Batch fetched {$objectType}", [
'requested_count' => count($crmIds),
'returned_count' => count($results),
'crm_ids' => $crmIds,
]);
}
private function handleBatchError(\Throwable $e, string $objectType, array $crmIds): void
{
$errorMessage = $e->getMessage() ?: 'Unknown error';
$errorTrace = $e->getTraceAsString() ?: 'No trace available';
$this->log->error("[HubSpot] Failed to batch fetch {$objectType}", [
'crm_ids' => $crmIds,
'error' => $errorMessage,
'trace' => $errorTrace,
]);
throw new CrmException("Failed to batch fetch {$objectType}: " . $errorMessage);
}
/**
* Batch read multiple opportunities by their CRM IDs
*
* @param array<string> $crmIds Array of HubSpot deal IDs (max 100)
* @param array<string> $fields Array of property names to fetch
*
* @return array<string, array> Array keyed by CRM ID with opportunity data
*/
public function getOpportunitiesByIds(array $crmIds, array $fields): array
{
return $this->batchReadObjects('deals', $crmIds, $fields);
}
/**
* Batch read multiple companies by their CRM IDs
*
* @param array<string> $crmIds Array of HubSpot company IDs (max 100)
* @param array<string> $fields Array of property names to fetch
*
* @return array<string, array> Array keyed by CRM ID with company data
*/
public function getCompaniesByIds(array $crmIds, array $fields): array
{
return $this->batchReadObjects('companies', $crmIds, $fields);
}
/**
* Batch read multiple contacts by their CRM IDs
*
* @param array<string> $crmIds Array of HubSpot contact IDs (max 100)
* @param array<string> $fields Array of property names to fetch
*
* @return array<string, array> Array keyed by CRM ID with contact data
*/
public function getContactsByIds(array $crmIds, array $fields): array
{
return $this->batchReadObjects('contacts', $crmIds, $fields);
}
/**
* @throws CompanyApiException
* @throws CrmException
*/
public function getAccountById(string $crmId, array $fields): array
{
try {
$company = $this->getNewInstance()->crm()->companies()->basicApi()->getById(
$crmId,
implode(',', $fields),
);
} catch (CompanyApiException $e) {
$this->log->info('[Hubspot] Failed to fetch account', [
'crm_id' => $crmId,
'reason' => $e->getMessage(),
]);
throw $e;
}
if (! $company instanceof CompaniesWithAssociations) {
throw new CrmException('Account not found');
}
return [
'id' => $company->getId(),
'properties' => $company->getProperties(),
];
}
/**
* @throws ContactApiException
* @throws CrmException
*/
public function getContactById(string $crmId, array $fields): array
{
try {
$contact = $this->getNewInstance()->crm()->contacts()->basicApi()->getById(
$crmId,
implode(',', $fields)
);
} catch (ContactApiException $e) {
$this->log->info('[Hubspot] Failed to fetch contact', [
'crm_id' => $crmId,
'reason' => $e->getMessage(),
]);
throw $e;
}
if (! $contact instanceof ContactsWithAssociations) {
throw new CrmException('Contact not found');
}
return [
'id' => $contact->getId(),
'properties' => $contact->getProperties(),
];
}
/**
* This is email search request that Hubspot offers as GET (more generous quota)
*/
public function getContactByEmail(string $email, array $fields = []): array
{
try {
$contact = $this->getNewInstance()->crm()->contacts()->basicApi()->getById(
$email,
implode(',', $fields),
null,
false,
'email'
);
return [
'id' => $contact->getId(),
'properties' => $contact->getProperties(),
];
} catch (ContactApiException $e) {
$this->log->info('[Hubspot] Failed to fetch contact', [
'email' => $email,
'reason' => $e->getMessage(),
]);
return [];
}
}
/**
* @throws CrmException
*/
public function fetchProperty(string $objectType, string $propertyId): Property
{
$result = $this->getNewInstance()->crm()->properties()->coreApi()->getByName($objectType, $propertyId);
if (! $result instanceof Property) {
$this->log->error('[Hubspot] Failed to fetch property', [
'object_type' => $objectType,
'property_id' => $propertyId,
'reason' => $result->getMessage(),
]);
throw new CrmException('Failed to fetch property');
}
return $result;
}
/**
* @return array<CrmFieldOption>
*/
public function fetchPropertyOptions(string $objectType, string $propertyId): array
{
/** @var array<CrmFieldOption> */
return $this->fetchProperty($objectType, $propertyId)->getOptions();
}
/**
* @return array<array{id:string, label:string, deleted:bool}>
*/
public function fetchCallDispositions(): array
{
/** @var Response $response */
$response = $this->getInstance()->engagements()->getCallDispositions();
/**
* @var array<array{
* id:string,
* label:string,
* deleted: bool
* }>
*/
return $response->toArray();
}
/**
* @return array<CrmFieldOption>
*/
public function fetchOpportunityPipelineStages(): array
{
$stages = [];
$apiResponse = $this->getNewInstance()->crm()->pipelines()->pipelinesApi()->getAll('deals');
if ($apiResponse instanceof Error) {
$this->log->error('[Hubspot] Failed to fetch opportunity pipelines', [
'reason' => $apiResponse->getMessage(),
]);
return [];
}
foreach ($apiResponse->getResults() as $pipeline) {
$pipelineStages = array_map(
static function (PipelineStage $stage) {
return [
'id' => $stage->getId(),
'label' => $stage->getLabel(),
];
},
$pipeline->getStages()
);
$stages = array_merge($stages, $pipelineStages);
}
return $stages;
}
public function fetchOpportunityPipelines(): array
{
$pipelines = [];
try {
$apiResponse = $this->makeRequest('/crm/v3/pipelines/deals');
} catch (\Exception $e) {
$this->log->info('[Hubspot] Failed to fetch opportunity pipelines', [
'reason' => $e->getMessage(),
]);
return [];
}
$response = $apiResponse->toArray();
foreach ($response['results'] as $pipeline) {
$pipelines[] = [
'id' => $pipeline['id'],
'label' => $pipeline['label'],
];
}
return $pipelines;
}
/**
* @return array<CrmFieldOption>
*/
public function fetchMeetingOutcomeFieldOptions(Field $field): array
{
return $field->getCrmProviderId() === 'meetingOutcome'
? $this->fetchMeetingOutcomeTypes()
: $this->fetchCallActivityTypes();
}
public function fetchMeetingOutcomeTypes(): array
{
return $this->extractMeetingTypeOptions(
'[URL_WITH_CREDENTIALS] Response $response */
$response = $this->getInstance()
->getClient()
->request('GET', $endpoint);
/**
* @var array<array{
* value: string,
* label: string,
* displayOrder: int
* }> $optionData
*/
$optionData = $response->toArray()['options'] ?? [];
$options = [];
foreach ($optionData as $item) {
$options[] = [
'id' => $item['value'],
'value' => $item['value'],
'label' => $item['label'],
'display_order' => $item['displayOrder'],
];
}
return $options;
}
/**
* @return array<CrmFieldOption>
*/
public function fetchDispositionFieldOptions(): array
{
$options = [];
$dispositions = $this->fetchCallDispositions();
foreach ($dispositions as $disposition) {
if ($disposition['deleted'] !== false) {
continue;
}
$option['value'] = $disposition['id'];
$option['id'] = $disposition['id'];
$option['label'] = $disposition['label'];
$options[] = $option;
}
return $options;
}
/**
* @return array<CrmFieldOption>
*/
public function fetchOpportunityFieldOptions(Field $field): array
{
if ($field->isStageField()) {
return $this->fetchOpportunityPipelineStages();
}
if ($field->isPipelineField()) {
return $this->fetchOpportunityPipelines();
}
return $this->fetchPropertyOptions('deals', $field->getCrmProviderId());
}
/**
* @throws BadRequest
* @throws HubspotException
*/
public function makeRequest(string $endpoint, $method = 'GET', $payload = [], ?string $queryString = null)
{
$endpoint = self::BASE_URL . $endpoint;
if ($method === 'GET') {
return $this->getInstance()->getClient()?->request(
method: $method,
endpoint: $endpoint,
query_string: $queryString
);
} else {
return $this->getInstance()->getClient()->request($method, $endpoint, [
'json' => ($payload),
]);
}
}
/**
* @throws BadRequest
* @throws HubspotException
*/
public function createMeeting(array $payload): Response
{
$endpoint = '/crm/v3/objects/meetings';
return $this->makeRequest($endpoint, 'POST', $payload);
}
/**
* @throws BadRequest
* @throws HubspotException
*/
public function updateMeeting(string $meetingId, array $payload): Response
{
$endpoint = '/crm/v3/objects/meetings/' . $meetingId;
return $this->makeRequest($endpoint, 'PATCH', $payload);
}
/**
* @throws \Exception
*/
public function createNote(
string $body,
string $ownerId,
int $timestamp,
string $objectId,
NoteObject $noteObject
): ?string {
try {
$noteInput = new SimplePublicObjectInput([
'properties' => [
'hs_note_body' => $body,
'hubspot_owner_id' => $ownerId,
'hs_timestamp' => $timestamp,
],
]);
// Create note
$note = $this->getNewInstance()->crm()->objects()->basicApi()->create('note', $noteInput);
$this->getNewInstance()->crm()->objects()->associationsApi()->create(
'note',
$note->getId(),
$this->getNoteObject($noteObject),
$objectId,
$this->getNoteAssociationType($noteObject),
);
return $note->getId();
} catch (\Exception $e) {
$this->log->error('[Hubspot] Failed to create note', [
'objectId' => $objectId,
'noteObject' => $noteObject->getObjectType(),
'reason' => $e->getMessage(),
]);
\Sentry::captureException($e);
}
return null;
}
public function updateEngagement(string $objectId, array $engagement, array $metadata): void
{
$this->getInstance()->engagements()->update($objectId, $engagement, $metadata);
}
public function getEngagementData(string $engagementId): array
{
$engagement = $this->getInstance()->engagements()->get($engagementId);
return $engagement->toArray();
}
public function createEngagement(array $engagement, array $associations, array $metadata): Response
{
return $this->getInstance()
->engagements()
->create($engagement, $associations, $metadata);
}
public function isUnauthorizedException(\Exception $e): bool
{
// Check for specific HubSpot API exception types first
if ($e instanceof BadRequest) {
// BadRequest can contain 401 status codes
return $e->getCode() === 401;
}
// Check for HTTP client exceptions with status codes
if ($e instanceof \GuzzleHttp\Exception\RequestException && $e->hasResponse()) {
$response = $e->getResponse();
if ($response !== null) {
return $response->getStatusCode() === 401;
}
}
// Check for Guzzle HTTP exceptions
if ($e instanceof \GuzzleHttp\Exception\ClientException) {
return $e->getCode() === 401;
}
// Fallback to string matching as last resort, but be more specific
$message = strtolower($e->getMessage());
return str_contains($message, '401 unauthorized') ||
str_contains($message, 'http 401') ||
str_contains($message, 'status code 401') ||
(preg_match('/\b401\b/', $message) && str_contains($message, 'unauthorized'));
}
/**
* Validates and refreshes the access token if needed before API requests.
* This ensures long-running processes don't fail due to token expiration.
*
* @throws SocialAccountTokenInvalidException
*/
public function ensureValidToken(): void
{
if ($this->oauthAccount === null) {
return;
}
$newToken = $this->tokenManager->ensureValidToken($this->oauthAccount);
if ($newToken !== null) {
$this->accessToken = $newToken;
}
}
public function getConfig()
{
return $this->config;
}
// returns only active (archived=false)
public function getOwners(): array
{
return $this->getNewInstance()->crm()->owners()->getAll();
}
/**
* @param bool $archived
*
* @return array<Owner>|[]
*/
public function getOwnersArchived(bool $archived = true): array
{
$endpoint = '/crm/v3/owners';
$queryParams = [
'archived' => $archived ? 'true' : 'false',
];
$queryString = http_build_query($queryParams);
$owners = [];
try {
$response = $this->makeRequest(endpoint: $endpoint, queryString: $queryString);
$responseData = $response?->toArray();
foreach ($responseData['results'] as $result) {
try {
$owners[] = Owner::create($result);
} catch (Throwable $e) {
$this->log->error('[HubSpot] Failed to process owner data', [
'result' => $result,
'error' => $e->getMessage(),
]);
continue;
}
}
} catch (Throwable $e) {
$this->log->error('HubSpot] Failed to fetch owners', [
'archived' => $archived,
'error' => $e->getMessage(),
]);
return [];
}
return $owners;
}
public function getMeeting(string $engagementId): ObjectWithAssociations
{
return $this->getNewInstance()->crm()->objects()->basicApi()
->getById('meeting', $engagementId, null, 'contact,company,deal');
}
public function deleteEngagement(string $engagementId): void
{
$this->getInstance()->engagements()->delete((int) $engagementId);
}
public function getAssociationsData(array $ids, string $fromObject, string $toObject): array
{
$associationData = [];
$idChunks = array_chunk($ids, self::ASSOCIATIONS_BATCH_SIZE_LIMIT);
foreach ($idChunks as $idChunk) {
try {
$batchInput = new \HubSpot\Client\Crm\Associations\Model\BatchInputPublicObjectId();
$batchInput->setInputs(array_map(function ($id) {
$publicObjectId = new \HubSpot\Client\Crm\Associations\Model\PublicObjectId();
$publicObjectId->setId($id);
return $publicObjectId;
}, $idChunk));
$associatedObjectsData = $this
->getNewInstance()
->crm()
->associations()
->batchApi()
->read($fromObject, $toObject, $batchInput);
if ($associatedObjectsData instanceof \HubSpot\Client\Crm\Associations\Model\BatchResponsePublicAssociationMulti) {
foreach ($associatedObjectsData->getResults() as $association) {
$from = $association->getFrom()->getId();
$toAssociations = $association->getTo();
if (! empty($toAssociations)) {
$associationData[$from] = array_map(function ($item) {
return $item->getId();
}, $toAssociations);
}
}
}
} catch (\Exception $e) {
$this->log->error('[Hubspot] Failed to fetch associations', [
'from_object' => $fromObject,
'to_object' => $toObject,
'reason' => $e->getMessage(),
]);
}
}
return $associationData;
}
/**
* @throws \Exception
*/
private function getNoteAssociationType(NoteObject $noteObject): string
{
return match($noteObject) {
NoteObject::Opportunity => 'note_to_deal',
NoteObject::Lead, NoteObject::Contact => 'note_to_contact', // or 'note_to_lead' if your portal supports it
NoteObject::Account => 'note_to_company',
NoteObject::Call, NoteObject::Event => throw new \Exception('Not supported'),
};
}
/**
* @throws \Exception
*/
private function getNoteObject(NoteObject $noteObject): string
{
return match($noteObject) {
NoteObject::Opportunity => 'deal',
NoteObject::Lead, NoteObject::Contact => 'contact',
NoteObject::Account => 'company',
NoteObject::Call, NoteObject::Event => throw new \Exception('Not supported'),
};
}
public function addAssociations(string $objectType, string $associationType, array $payload): Response
{
$endpoint = "/crm/v4/associations/$objectType/$associationType/batch/create";
return $this->makeRequest($endpoint, 'POST', $payload);
}
public function removeAssociations(string $objectType, string $associationType, array $payload): Response
{
$endpoint = "/crm/v4/associations/$objectType/$associationType/batch/archive";
return $this->makeRequest($endpoint, 'POST', $payload);
}
}
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# [PASSWORD_DOTS] HS [PASSWORD_DOTS]
select * from teams where id = 2; # 2
select * from features; # 2
select * from team_features where team_id = 2; # 2
select * from crm_configurations where id = 2; # 2
select * from users where team_id = 2; #
select * from playbooks where team_id = 2; # event 38
select * from playbook_categories where playbook_id = 38; #
SELECT * FROM activities WHERE crm_configuration_id = 2 and crm_provider_id is not null order by id desc;
[URL_WITH_CREDENTIALS] CrmFieldOption array{id:string, label:string, value?:string}
*/
class Client extends BaseClient implements HubspotClientInterface
{
public const string MIN_API_VERSION = '2';
public const string BASE_URL = '[URL_WITH_CREDENTIALS] T
* @param callable(): T $apiCall
* @return T
*
* @throws RateLimitException
*/
private function executeRequest(callable $apiCall)
{
if (! $this->rateLimiter->canMakeRequest($this->config)) {
$retryAfter = $this->rateLimiter->requestAvailableIn($this->config);
$this->log->warning('[Hubspot] Rate limit exceeded, deferring request', [
'team_id' => $this->config->team_id,
'config_id' => $this->config->getId(),
'retry_after' => $retryAfter,
]);
throw new RateLimitException(
'Hubspot rate limit reached for configuration ' . $this->config->getId(),
$retryAfter,
);
}
$this->rateLimiter->incrementRequestCount($this->config);
try {
return $apiCall();
} catch (Throwable $e) {
if ($this->isHubspotRateLimit($e)) {
$retryAfter = $this->parseRetryAfter($e);
$this->log->warning('[Hubspot] Received 429 from API', [
'team_id' => $this->config->team_id,
'config_id' => $this->config->getId(),
'retry_after' => $retryAfter,
'reason' => $e->getMessage(),
]);
throw new RateLimitException('Hubspot returned 429', $retryAfter, $e);
}
throw $e;
}
}
private function isHubspotRateLimit(Throwable $e): bool
{
return method_exists($e, 'getCode') && (int) $e->getCode() === 429;
}
private function parseRetryAfter(Throwable $e): int
{
if (method_exists($e, 'getResponseHeaders')) {
$headers = $e->getResponseHeaders() ?: [];
$value = $headers['Retry-After'] ?? $headers['retry-after'] ?? null;
if (is_array($value)) {
$value = $value[0] ?? null;
}
if (is_numeric($value)) {
return (int) $value;
}
}
return 10;
}
public function getMinimumApiVersion(): string
{
return self::MIN_API_VERSION;
}
public function getInstance(): Factory
{
return new Factory([
'key' => $this->accessToken,
'oauth2' => true,
'base_url' => $this->baseUrl,
]);
}
public function getNewInstance(): Discovery
{
return \HubSpot\Factory::createWithAccessToken($this->accessToken);
}
/**
* Secondly and daily limits for Hubspot API
*
* Product Tier: Free & Starter | Professional & Enterprise | API add-on (any tier)
* Burst: 100/10 seconds | 150/10 seconds | 200/10 seconds
* Daily: 250,000 | 500,000 | 1,000,000
*
* Official documentation states: The search endpoints are rate limited to five requests per second.
* Since with 5 RPS were still hitting secondly rate limits we lowered it to 4
*/
public function getPaginatedData(array $payload, string $type, int $offset = 0): array
{
$total = 0;
$lastId = null;
$rows = [];
foreach ($this->getPaginatedDataGenerator($payload, $type, $offset, $total, $lastId) as $row) {
$rows[] = $row;
}
return ['results' => $rows, 'total' => $total, 'last_record' => $lastId];
}
/**
* @throws HubspotException
* @throws SocialAccountTokenInvalidException
* @throws BadRequest
*/
public function getPaginatedDataGenerator(
array $payload,
string $type,
int $offset = 0,
int &$total = 0,
?string &$lastRecordId = null
): \Generator {
return $this->paginationService->getPaginatedDataGenerator(
$this,
$payload,
$type,
$offset,
$total,
$lastRecordId
);
}
/**
* @throws DealApiException
* @throws CrmException
*/
public function getOpportunityById(string $crmId, array $fields): array
{
try {
$deal = $this->executeRequest(fn () => $this->getNewInstance()->crm()->deals()->basicApi()->getById(
$crmId,
implode(',', $fields),
'companies,contacts'
));
} catch (DealApiException $e) {
$this->log->info('[Hubspot] Failed to fetch opportunity', [
'crm_id' => $crmId,
'reason' => $e->getMessage(),
]);
throw $e;
}
if (! $deal instanceof DealWithAssociations) {
throw new CrmException('Deal not found');
}
return [
'id' => $deal->getId(),
'properties' => $deal->getProperties(),
'associations' => $deal->getAssociations(),
];
}
/**
* Generic batch read method for HubSpot objects
*
* @param string $objectType The object type ('deals', 'companies', 'contacts')
* @param array<string> $crmIds Array of HubSpot object IDs (max 100)
* @param array<string> $fields Array of property names to fetch
*
* @return array<string, array> Array keyed by CRM ID with object data
*/
private function batchReadObjects(string $objectType, array $crmIds, array $fields): array
{
if (empty($crmIds)) {
return [];
}
$this->validateBatchSize($objectType, $crmIds);
$this->ensureValidToken();
try {
$batchConfig = $this->createBatchConfiguration($objectType);
$batchReadRequest = $this->prepareBatchRequest($batchConfig, $crmIds, $fields);
$response = $batchConfig['api']->read($batchReadRequest);
$this->validateApiResponse($response, $objectType);
$results = $this->processApiResults($response);
$this->logBatchResults($objectType, $crmIds, $results);
return $results;
} catch (\Throwable $e) {
$this->handleBatchError($e, $objectType, $crmIds);
}
}
private function validateBatchSize(string $objectType, array $crmIds): void
{
if (count($crmIds) > 100) {
throw new \InvalidArgumentException("Batch size cannot exceed 100 {$objectType}");
}
}
private function createBatchConfiguration(string $objectType): array
{
$configurations = [
'deals' => [
'batchReadRequest' => new \HubSpot\Client\Crm\Deals\Model\BatchReadInputSimplePublicObjectId(),
'inputClass' => \HubSpot\Client\Crm\Deals\Model\SimplePublicObjectId::class,
'api' => $this->getNewInstance()->crm()->deals()->batchApi(),
],
'companies' => [
'batchReadRequest' => new \HubSpot\Client\Crm\Companies\Model\BatchReadInputSimplePublicObjectId(),
'inputClass' => \HubSpot\Client\Crm\Companies\Model\SimplePublicObjectId::class,
'api' => $this->getNewInstance()->crm()->companies()->batchApi(),
],
'contacts' => [
'batchReadRequest' => new \HubSpot\Client\Crm\Contacts\Model\BatchReadInputSimplePublicObjectId(),
'inputClass' => \HubSpot\Client\Crm\Contacts\Model\SimplePublicObjectId::class,
'api' => $this->getNewInstance()->crm()->contacts()->batchApi(),
],
];
if (! isset($configurations[$objectType])) {
throw new \InvalidArgumentException("Unsupported object type: {$objectType}");
}
return $configurations[$objectType];
}
private function prepareBatchRequest(array $batchConfig, array $crmIds, array $fields): object
{
$batchReadRequest = $batchConfig['batchReadRequest'];
$inputClass = $batchConfig['inputClass'];
$inputs = array_map(function ($crmId) use ($inputClass) {
$input = new $inputClass();
$input->setId($crmId);
return $input;
}, $crmIds);
$batchReadRequest->setInputs($inputs);
$batchReadRequest->setProperties($fields);
return $batchReadRequest;
}
private function validateApiResponse($response, string $objectType): void
{
if (! $response) {
throw new CrmException("HubSpot API returned null response for {$objectType} batch read");
}
}
private function processApiResults($response): array
{
$results = [];
$responseResults = $response->getResults();
if ($responseResults) {
foreach ($responseResults as $object) {
if ($object && $object->getId()) {
$results[$object->getId()] = [
'id' => $object->getId(),
'properties' => $object->getProperties() ?: [],
];
}
}
}
return $results;
}
private function logBatchResults(string $objectType, array $crmIds, array $results): void
{
$this->log->info("[HubSpot] Batch fetched {$objectType}", [
'requested_count' => count($crmIds),
'returned_count' => count($results),
'crm_ids' => $crmIds,
]);
}
private function handleBatchError(\Throwable $e, string $objectType, array $crmIds): void
{
$errorMessage = $e->getMessage() ?: 'Unknown error';
$errorTrace = $e->getTraceAsString() ?: 'No trace available';
$this->log->error("[HubSpot] Failed to batch fetch {$objectType}", [
'crm_ids' => $crmIds,
'error' => $errorMessage,
'trace' => $errorTrace,
]);
throw new CrmException("Failed to batch fetch {$objectType}: " . $errorMessage);
}
/**
* Batch read multiple opportunities by their CRM IDs
*
* @param array<string> $crmIds Array of HubSpot deal IDs (max 100)
* @param array<string> $fields Array of property names to fetch
*
* @return array<string, array> Array keyed by CRM ID with opportunity data
*/
public function getOpportunitiesByIds(array $crmIds, array $fields): array
{
return $this->batchReadObjects('deals', $crmIds, $fields);
}
/**
* Batch read multiple companies by their CRM IDs
*
* @param array<string> $crmIds Array of HubSpot company IDs (max 100)
* @param array<string> $fields Array of property names to fetch
*
* @return array<string, array> Array keyed by CRM ID with company data
*/
public function getCompaniesByIds(array $crmIds, array $fields): array
{
return $this->batchReadObjects('companies', $crmIds, $fields);
}
/**
* Batch read multiple contacts by their CRM IDs
*
* @param array<string> $crmIds Array of HubSpot contact IDs (max 100)
* @param array<string> $fields Array of property names to fetch
*
* @return array<string, array> Array keyed by CRM ID with contact data
*/
public function getContactsByIds(array $crmIds, array $fields): array
{
return $this->batchReadObjects('contacts', $crmIds, $fields);
}
/**
* @throws CompanyApiException
* @throws CrmException
*/
public function getAccountById(string $crmId, array $fields): array
{
try {
$company = $this->getNewInstance()->crm()->companies()->basicApi()->getById(
$crmId,
implode(',', $fields),
);
} catch (CompanyApiException $e) {
$this->log->info('[Hubspot] Failed to fetch account', [
'crm_id' => $crmId,
'reason' => $e->getMessage(),
]);
throw $e;
}
if (! $company instanceof CompaniesWithAssociations) {
throw new CrmException('Account not found');
}
return [
'id' => $company->getId(),
'properties' => $company->getProperties(),
];
}
/**
* @throws ContactApiException
* @throws CrmException
*/
public function getContactById(string $crmId, array $fields): array
{
try {
$contact = $this->getNewInstance()->crm()->contacts()->basicApi()->getById(
$crmId,
implode(',', $fields)
);
} catch (ContactApiException $e) {
$this->log->info('[Hubspot] Failed to fetch contact', [
'crm_id' => $crmId,
'reason' => $e->getMessage(),
]);
throw $e;
}
if (! $contact instanceof ContactsWithAssociations) {
throw new CrmException('Contact not found');
}
return [
'id' => $contact->getId(),
'properties' => $contact->getProperties(),
];
}
/**
* This is email search request that Hubspot offers as GET (more generous quota)
*/
public function getContactByEmail(string $email, array $fields = []): array
{
try {
$contact = $this->getNewInstance()->crm()->contacts()->basicApi()->getById(
$email,
implode(',', $fields),
null,
false,
'email'
);
return [
'id' => $contact->getId(),
'properties' => $contact->getProperties(),
];
} catch (ContactApiException $e) {
$this->log->info('[Hubspot] Failed to fetch contact', [
'email' => $email,
'reason' => $e->getMessage(),
]);
return [];
}
}
/**
* @throws CrmException
*/
public function fetchProperty(string $objectType, string $propertyId): Property
{
$result = $this->getNewInstance()->crm()->properties()->coreApi()->getByName($objectType, $propertyId);
if (! $result instanceof Property) {
$this->log->error('[Hubspot] Failed to fetch property', [
'object_type' => $objectType,
'property_id' => $propertyId,
'reason' => $result->getMessage(),
]);
throw new CrmException('Failed to fetch property');
}
return $result;
}
/**
* @return array<CrmFieldOption>
*/
public function fetchPropertyOptions(string $objectType, string $propertyId): array
{
/** @var array<CrmFieldOption> */
return $this->fetchProperty($objectType, $propertyId)->getOptions();
}
/**
* @return array<array{id:string, label:string, deleted:bool}>
*/
public function fetchCallDispositions(): array
{
/** @var Response $response */
$response = $this->getInstance()->engagements()->getCallDispositions();
/**
* @var array<array{
* id:string,
* label:string,
* deleted: bool
* }>
*/
return $response->toArray();
}
/**
* @return array<CrmFieldOption>
*/
public function fetchOpportunityPipelineStages(): array
{
$stages = [];
$apiResponse = $this->getNewInstance()->crm()->pipelines()->pipelinesApi()->getAll('deals');
if ($apiResponse instanceof Error) {
$this->log->error('[Hubspot] Failed to fetch opportunity pipelines', [
'reason' => $apiResponse->getMessage(),
]);
return [];
}
foreach ($apiResponse->getResults() as $pipeline) {
$pipelineStages = array_map(
static function (PipelineStage $stage) {
return [
'id' => $stage->getId(),
'label' => $stage->getLabel(),
];
},
$pipeline->getStages()
);
$stages = array_merge($stages, $pipelineStages);
}
return $stages;
}
public function fetchOpportunityPipelines(): array
{
$pipelines = [];
try {
$apiResponse = $this->makeRequest('/crm/v3/pipelines/deals');
} catch (\Exception $e) {
$this->log->info('[Hubspot] Failed to fetch opportunity pipelines', [
'reason' => $e->getMessage(),
]);
return [];
}
$response = $apiResponse->toArray();
foreach ($response['results'] as $pipeline) {
$pipelines[] = [
'id' => $pipeline['id'],
'label' => $pipeline['label'],
];
}
return $pipelines;
}
/**
* @return array<CrmFieldOption>
*/
public function fetchMeetingOutcomeFieldOptions(Field $field): array
{
return $field->getCrmProviderId() === 'meetingOutcome'
? $this->fetchMeetingOutcomeTypes()
: $this->fetchCallActivityTypes();
}
public function fetchMeetingOutcomeTypes(): array
{
return $this->extractMeetingTypeOptions(
'[URL_WITH_CREDENTIALS] Response $response */
$response = $this->getInstance()
->getClient()
->request('GET', $endpoint);
/**
* @var array<array{
* value: string,
* label: string,
* displayOrder: int
* }> $optionData
*/
$optionData = $response->toArray()['options'] ?? [];
$options = [];
foreach ($optionData as $item) {
$options[] = [
'id' => $item['value'],
'value' => $item['value'],
'label' => $item['label'],
'display_order' => $item['displayOrder'],
];
}
return $options;
}
/**
* @return array<CrmFieldOption>
*/
public function fetchDispositionFieldOptions(): array
{
$options = [];
$dispositions = $this->fetchCallDispositions();
foreach ($dispositions as $disposition) {
if ($disposition['deleted'] !== false) {
continue;
}
$option['value'] = $disposition['id'];
$option['id'] = $disposition['id'];
$option['label'] = $disposition['label'];
$options[] = $option;
}
return $options;
}
/**
* @return array<CrmFieldOption>
*/
public function fetchOpportunityFieldOptions(Field $field): array
{
if ($field->isStageField()) {
return $this->fetchOpportunityPipelineStages();
}
if ($field->isPipelineField()) {
return $this->fetchOpportunityPipelines();
}
return $this->fetchPropertyOptions('deals', $field->getCrmProviderId());
}
/**
* @throws BadRequest
* @throws HubspotException
*/
public function makeRequest(string $endpoint, $method = 'GET', $payload = [], ?string $queryString = null)
{
$endpoint = self::BASE_URL . $endpoint;
if ($method === 'GET') {
return $this->getInstance()->getClient()?->request(
method: $method,
endpoint: $endpoint,
query_string: $queryString
);
} else {
return $this->getInstance()->getClient()->request($method, $endpoint, [
'json' => ($payload),
]);
}
}
/**
* @throws BadRequest
* @throws HubspotException
*/
public function createMeeting(array $payload): Response
{
$endpoint = '/crm/v3/objects/meetings';
return $this->makeRequest($endpoint, 'POST', $payload);
}
/**
* @throws BadRequest
* @throws HubspotException
*/
public function updateMeeting(string $meetingId, array $payload): Response
{
$endpoint = '/crm/v3/objects/meetings/' . $meetingId;
return $this->makeRequest($endpoint, 'PATCH', $payload);
}
/**
* @throws \Exception
*/
public function createNote(
string $body,
string $ownerId,
int $timestamp,
string $objectId,
NoteObject $noteObject
): ?string {
try {
$noteInput = new SimplePublicObjectInput([
'properties' => [
'hs_note_body' => $body,
'hubspot_owner_id' => $ownerId,
'hs_timestamp' => $timestamp,
],
]);
// Create note
$note = $this->getNewInstance()->crm()->objects()->basicApi()->create('note', $noteInput);
$this->getNewInstance()->crm()->objects()->associationsApi()->create(
'note',
$note->getId(),
$this->getNoteObject($noteObject),
$objectId,
$this->getNoteAssociationType($noteObject),
);
return $note->getId();
} catch (\Exception $e) {
$this->log->error('[Hubspot] Failed to create note', [
'objectId' => $objectId,
'noteObject' => $noteObject->getObjectType(),
'reason' => $e->getMessage(),
]);
\Sentry::captureException($e);
}
return null;
}
public function updateEngagement(string $objectId, array $engagement, array $metadata): void
{
$this->getInstance()->engagements()->update($objectId, $engagement, $metadata);
}
public function getEngagementData(string $engagementId): array
{
$engagement = $this->getInstance()->engagements()->get($engagementId);
return $engagement->toArray();
}
public function createEngagement(array $engagement, array $associations, array $metadata): Response
{
return $this->getInstance()
->engagements()
->create($engagement, $associations, $metadata);
}
public function isUnauthorizedException(\Exception $e): bool
{
// Check for specific HubSpot API exception types first
if ($e instanceof BadRequest) {
// BadRequest can contain 401 status codes
return $e->getCode() === 401;
}
// Check for HTTP client exceptions with status codes
if ($e instanceof \GuzzleHttp\Exception\RequestException && $e->hasResponse()) {
$response = $e->getResponse();
if ($response !== null) {
return $response->getStatusCode() === 401;
}
}
// Check for Guzzle HTTP exceptions
if ($e instanceof \GuzzleHttp\Exception\ClientException) {
return $e->getCode() === 401;
}
// Fallback to string matching as last resort, but be more specific
$message = strtolower($e->getMessage());
return str_contains($message, '401 unauthorized') ||
str_contains($message, 'http 401') ||
str_contains($message, 'status code 401') ||
(preg_match('/\b401\b/', $message) && str_contains($message, 'unauthorized'));
}
/**
* Validates and refreshes the access token if needed before API requests.
* This ensures long-running processes don't fail due to token expiration.
*
* @throws SocialAccountTokenInvalidException
*/
public function ensureValidToken(): void
{
if ($this->oauthAccount === null) {
return;
}
$newToken = $this->tokenManager->ensureValidToken($this->oauthAccount);
if ($newToken !== null) {
$this->accessToken = $newToken;
}
}
public function getConfig()
{
return $this->config;
}
// returns only active (archived=false)
public function getOwners(): array
{
return $this->getNewInstance()->crm()->owners()->getAll();
}
/**
* @param bool $archived
*
* @return array<Owner>|[]
*/
public function getOwnersArchived(bool $archived = true): array
{
$endpoint = '/crm/v3/owners';
$queryParams = [
'archived' => $archived ? 'true' : 'false',
];
$queryString = http_build_query($queryParams);
$owners = [];
try {
$response = $this->makeRequest(endpoint: $endpoint, queryString: $queryString);
$responseData = $response?->toArray();
foreach ($responseData['results'] as $result) {
try {
$owners[] = Owner::create($result);
} catch (Throwable $e) {
$this->log->error('[HubSpot] Failed to process owner data', [
'result' => $result,
'error' => $e->getMessage(),
]);
continue;
}
}
} catch (Throwable $e) {
$this->log->error('HubSpot] Failed to fetch owners', [
'archived' => $archived,
'error' => $e->getMessage(),
]);
return [];
}
return $owners;
}
public function getMeeting(string $engagementId): ObjectWithAssociations
{
return $this->getNewInstance()->crm()->objects()->basicApi()
->getById('meeting', $engagementId, null, 'contact,company,deal');
}
public function deleteEngagement(string $engagementId): void
{
$this->getInstance()->engagements()->delete((int) $engagementId);
}
public function getAssociationsData(array $ids, string $fromObject, string $toObject): array
{
$associationData = [];
$idChunks = array_chunk($ids, self::ASSOCIATIONS_BATCH_SIZE_LIMIT);
foreach ($idChunks as $idChunk) {
try {
$batchInput = new \HubSpot\Client\Crm\Associations\Model\BatchInputPublicObjectId();
$batchInput->setInputs(array_map(function ($id) {
$publicObjectId = new \HubSpot\Client\Crm\Associations\Model\PublicObjectId();
$publicObjectId->setId($id);
return $publicObjectId;
}, $idChunk));
$associatedObjectsData = $this
->getNewInstance()
->crm()
->associations()
->batchApi()
->read($fromObject, $toObject, $batchInput);
if ($associatedObjectsData instanceof \HubSpot\Client\Crm\Associations\Model\BatchResponsePublicAssociationMulti) {
foreach ($associatedObjectsData->getResults() as $association) {
$from = $association->getFrom()->getId();
$toAssociations = $association->getTo();
if (! empty($toAssociations)) {
$associationData[$from] = array_map(function ($item) {
return $item->getId();
}, $toAssociations);
}
}
}
} catch (\Exception $e) {
$this->log->error('[Hubspot] Failed to fetch associations', [
'from_object' => $fromObject,
'to_object' => $toObject,
'reason' => $e->getMessage(),
]);
}
}
return $associationData;
}
/**
* @throws \Exception
*/
private function getNoteAssociationType(NoteObject $noteObject): string
{
return match($noteObject) {
NoteObject::Opportunity => 'note_to_deal',
NoteObject::Lead, NoteObject::Contact => 'note_to_contact', // or 'note_to_lead' if your portal supports it
NoteObject::Account => 'note_to_company',
NoteObject::Call, NoteObject::Event => throw new \Exception('Not supported'),
};
}
/**
* @throws \Exception
*/
private function getNoteObject(NoteObject $noteObject): string
{
return match($noteObject) {
NoteObject::Opportunity => 'deal',
NoteObject::Lead, NoteObject::Contact => 'contact',
NoteObject::Account => 'company',
NoteObject::Call, NoteObject::Event => throw new \Exception('Not supported'),
};
}
public function addAssociations(string $objectType, string $associationType, array $payload): Response
{
$endpoint = "/crm/v4/associations/$objectType/$associationType/batch/create";
return $this->makeRequest($endpoint, 'POST', $payload);
}
public function removeAssociations(string $objectType, string $associationType, array $payload): Response
{
$endpoint = "/crm/v4/associations/$objectType/$associationType/batch/archive";
return $this->makeRequest($endpoint, 'POST', $payload);
}
}
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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
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....
|
Claude
|
Claude
|
NULL
|
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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
Show more
10:42
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Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
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Give positive feedback
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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
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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....
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# [PASSWORD_DOTS] HS [PASSWORD_DOTS]
select * from teams where id = 2; # 2
select * from features; # 2
select * from team_features where team_id = 2; # 2
select * from crm_configurations where id = 2; # 2
select * from users where team_id = 2; #
select * from playbooks where team_id = 2; # event 38
select * from playbook_categories where playbook_id = 38; #
SELECT * FROM activities WHERE crm_configuration_id = 2 and crm_provider_id is not null order by id desc;
[URL_WITH_CREDENTIALS] CrmFieldOption array{id:string, label:string, value?:string}
*/
class Client extends BaseClient implements HubspotClientInterface
{
public const string MIN_API_VERSION = '2';
public const string BASE_URL = '[URL_WITH_CREDENTIALS] T
* @param callable(): T $apiCall
* @return T
*
* @throws RateLimitException
*/
private function executeRequest(callable $apiCall)
{
if (! $this->rateLimiter->canMakeRequest($this->config)) {
$retryAfter = $this->rateLimiter->requestAvailableIn($this->config);
$this->log->warning('[Hubspot] Rate limit exceeded, deferring request', [
'team_id' => $this->config->team_id,
'config_id' => $this->config->getId(),
'retry_after' => $retryAfter,
]);
throw new RateLimitException(
'Hubspot rate limit reached for configuration ' . $this->config->getId(),
$retryAfter,
);
}
$this->rateLimiter->incrementRequestCount($this->config);
try {
return $apiCall();
} catch (Throwable $e) {
if ($this->isHubspotRateLimit($e)) {
$retryAfter = $this->parseRetryAfter($e);
$this->log->warning('[Hubspot] Received 429 from API', [
'team_id' => $this->config->team_id,
'config_id' => $this->config->getId(),
'retry_after' => $retryAfter,
'reason' => $e->getMessage(),
]);
throw new RateLimitException('Hubspot returned 429', $retryAfter, $e);
}
throw $e;
}
}
private function isHubspotRateLimit(Throwable $e): bool
{
return method_exists($e, 'getCode') && (int) $e->getCode() === 429;
}
private function parseRetryAfter(Throwable $e): int
{
if (method_exists($e, 'getResponseHeaders')) {
$headers = $e->getResponseHeaders() ?: [];
$value = $headers['Retry-After'] ?? $headers['retry-after'] ?? null;
if (is_array($value)) {
$value = $value[0] ?? null;
}
if (is_numeric($value)) {
return (int) $value;
}
}
return 10;
}
public function getMinimumApiVersion(): string
{
return self::MIN_API_VERSION;
}
public function getInstance(): Factory
{
return new Factory([
'key' => $this->accessToken,
'oauth2' => true,
'base_url' => $this->baseUrl,
]);
}
public function getNewInstance(): Discovery
{
return \HubSpot\Factory::createWithAccessToken($this->accessToken);
}
/**
* Secondly and daily limits for Hubspot API
*
* Product Tier: Free & Starter | Professional & Enterprise | API add-on (any tier)
* Burst: 100/10 seconds | 150/10 seconds | 200/10 seconds
* Daily: 250,000 | 500,000 | 1,000,000
*
* Official documentation states: The search endpoints are rate limited to five requests per second.
* Since with 5 RPS were still hitting secondly rate limits we lowered it to 4
*/
public function getPaginatedData(array $payload, string $type, int $offset = 0): array
{
$total = 0;
$lastId = null;
$rows = [];
foreach ($this->getPaginatedDataGenerator($payload, $type, $offset, $total, $lastId) as $row) {
$rows[] = $row;
}
return ['results' => $rows, 'total' => $total, 'last_record' => $lastId];
}
/**
* @throws HubspotException
* @throws SocialAccountTokenInvalidException
* @throws BadRequest
*/
public function getPaginatedDataGenerator(
array $payload,
string $type,
int $offset = 0,
int &$total = 0,
?string &$lastRecordId = null
): \Generator {
return $this->paginationService->getPaginatedDataGenerator(
$this,
$payload,
$type,
$offset,
$total,
$lastRecordId
);
}
/**
* @throws DealApiException
* @throws CrmException
*/
public function getOpportunityById(string $crmId, array $fields): array
{
try {
$deal = $this->executeRequest(fn () => $this->getNewInstance()->crm()->deals()->basicApi()->getById(
$crmId,
implode(',', $fields),
'companies,contacts'
));
} catch (DealApiException $e) {
$this->log->info('[Hubspot] Failed to fetch opportunity', [
'crm_id' => $crmId,
'reason' => $e->getMessage(),
]);
throw $e;
}
if (! $deal instanceof DealWithAssociations) {
throw new CrmException('Deal not found');
}
return [
'id' => $deal->getId(),
'properties' => $deal->getProperties(),
'associations' => $deal->getAssociations(),
];
}
/**
* Generic batch read method for HubSpot objects
*
* @param string $objectType The object type ('deals', 'companies', 'contacts')
* @param array<string> $crmIds Array of HubSpot object IDs (max 100)
* @param array<string> $fields Array of property names to fetch
*
* @return array<string, array> Array keyed by CRM ID with object data
*/
private function batchReadObjects(string $objectType, array $crmIds, array $fields): array
{
if (empty($crmIds)) {
return [];
}
$this->validateBatchSize($objectType, $crmIds);
$this->ensureValidToken();
try {
$batchConfig = $this->createBatchConfiguration($objectType);
$batchReadRequest = $this->prepareBatchRequest($batchConfig, $crmIds, $fields);
$response = $batchConfig['api']->read($batchReadRequest);
$this->validateApiResponse($response, $objectType);
$results = $this->processApiResults($response);
$this->logBatchResults($objectType, $crmIds, $results);
return $results;
} catch (\Throwable $e) {
$this->handleBatchError($e, $objectType, $crmIds);
}
}
private function validateBatchSize(string $objectType, array $crmIds): void
{
if (count($crmIds) > 100) {
throw new \InvalidArgumentException("Batch size cannot exceed 100 {$objectType}");
}
}
private function createBatchConfiguration(string $objectType): array
{
$configurations = [
'deals' => [
'batchReadRequest' => new \HubSpot\Client\Crm\Deals\Model\BatchReadInputSimplePublicObjectId(),
'inputClass' => \HubSpot\Client\Crm\Deals\Model\SimplePublicObjectId::class,
'api' => $this->getNewInstance()->crm()->deals()->batchApi(),
],
'companies' => [
'batchReadRequest' => new \HubSpot\Client\Crm\Companies\Model\BatchReadInputSimplePublicObjectId(),
'inputClass' => \HubSpot\Client\Crm\Companies\Model\SimplePublicObjectId::class,
'api' => $this->getNewInstance()->crm()->companies()->batchApi(),
],
'contacts' => [
'batchReadRequest' => new \HubSpot\Client\Crm\Contacts\Model\BatchReadInputSimplePublicObjectId(),
'inputClass' => \HubSpot\Client\Crm\Contacts\Model\SimplePublicObjectId::class,
'api' => $this->getNewInstance()->crm()->contacts()->batchApi(),
],
];
if (! isset($configurations[$objectType])) {
throw new \InvalidArgumentException("Unsupported object type: {$objectType}");
}
return $configurations[$objectType];
}
private function prepareBatchRequest(array $batchConfig, array $crmIds, array $fields): object
{
$batchReadRequest = $batchConfig['batchReadRequest'];
$inputClass = $batchConfig['inputClass'];
$inputs = array_map(function ($crmId) use ($inputClass) {
$input = new $inputClass();
$input->setId($crmId);
return $input;
}, $crmIds);
$batchReadRequest->setInputs($inputs);
$batchReadRequest->setProperties($fields);
return $batchReadRequest;
}
private function validateApiResponse($response, string $objectType): void
{
if (! $response) {
throw new CrmException("HubSpot API returned null response for {$objectType} batch read");
}
}
private function processApiResults($response): array
{
$results = [];
$responseResults = $response->getResults();
if ($responseResults) {
foreach ($responseResults as $object) {
if ($object && $object->getId()) {
$results[$object->getId()] = [
'id' => $object->getId(),
'properties' => $object->getProperties() ?: [],
];
}
}
}
return $results;
}
private function logBatchResults(string $objectType, array $crmIds, array $results): void
{
$this->log->info("[HubSpot] Batch fetched {$objectType}", [
'requested_count' => count($crmIds),
'returned_count' => count($results),
'crm_ids' => $crmIds,
]);
}
private function handleBatchError(\Throwable $e, string $objectType, array $crmIds): void
{
$errorMessage = $e->getMessage() ?: 'Unknown error';
$errorTrace = $e->getTraceAsString() ?: 'No trace available';
$this->log->error("[HubSpot] Failed to batch fetch {$objectType}", [
'crm_ids' => $crmIds,
'error' => $errorMessage,
'trace' => $errorTrace,
]);
throw new CrmException("Failed to batch fetch {$objectType}: " . $errorMessage);
}
/**
* Batch read multiple opportunities by their CRM IDs
*
* @param array<string> $crmIds Array of HubSpot deal IDs (max 100)
* @param array<string> $fields Array of property names to fetch
*
* @return array<string, array> Array keyed by CRM ID with opportunity data
*/
public function getOpportunitiesByIds(array $crmIds, array $fields): array
{
return $this->batchReadObjects('deals', $crmIds, $fields);
}
/**
* Batch read multiple companies by their CRM IDs
*
* @param array<string> $crmIds Array of HubSpot company IDs (max 100)
* @param array<string> $fields Array of property names to fetch
*
* @return array<string, array> Array keyed by CRM ID with company data
*/
public function getCompaniesByIds(array $crmIds, array $fields): array
{
return $this->batchReadObjects('companies', $crmIds, $fields);
}
/**
* Batch read multiple contacts by their CRM IDs
*
* @param array<string> $crmIds Array of HubSpot contact IDs (max 100)
* @param array<string> $fields Array of property names to fetch
*
* @return array<string, array> Array keyed by CRM ID with contact data
*/
public function getContactsByIds(array $crmIds, array $fields): array
{
return $this->batchReadObjects('contacts', $crmIds, $fields);
}
/**
* @throws CompanyApiException
* @throws CrmException
*/
public function getAccountById(string $crmId, array $fields): array
{
try {
$company = $this->getNewInstance()->crm()->companies()->basicApi()->getById(
$crmId,
implode(',', $fields),
);
} catch (CompanyApiException $e) {
$this->log->info('[Hubspot] Failed to fetch account', [
'crm_id' => $crmId,
'reason' => $e->getMessage(),
]);
throw $e;
}
if (! $company instanceof CompaniesWithAssociations) {
throw new CrmException('Account not found');
}
return [
'id' => $company->getId(),
'properties' => $company->getProperties(),
];
}
/**
* @throws ContactApiException
* @throws CrmException
*/
public function getContactById(string $crmId, array $fields): array
{
try {
$contact = $this->getNewInstance()->crm()->contacts()->basicApi()->getById(
$crmId,
implode(',', $fields)
);
} catch (ContactApiException $e) {
$this->log->info('[Hubspot] Failed to fetch contact', [
'crm_id' => $crmId,
'reason' => $e->getMessage(),
]);
throw $e;
}
if (! $contact instanceof ContactsWithAssociations) {
throw new CrmException('Contact not found');
}
return [
'id' => $contact->getId(),
'properties' => $contact->getProperties(),
];
}
/**
* This is email search request that Hubspot offers as GET (more generous quota)
*/
public function getContactByEmail(string $email, array $fields = []): array
{
try {
$contact = $this->getNewInstance()->crm()->contacts()->basicApi()->getById(
$email,
implode(',', $fields),
null,
false,
'email'
);
return [
'id' => $contact->getId(),
'properties' => $contact->getProperties(),
];
} catch (ContactApiException $e) {
$this->log->info('[Hubspot] Failed to fetch contact', [
'email' => $email,
'reason' => $e->getMessage(),
]);
return [];
}
}
/**
* @throws CrmException
*/
public function fetchProperty(string $objectType, string $propertyId): Property
{
$result = $this->getNewInstance()->crm()->properties()->coreApi()->getByName($objectType, $propertyId);
if (! $result instanceof Property) {
$this->log->error('[Hubspot] Failed to fetch property', [
'object_type' => $objectType,
'property_id' => $propertyId,
'reason' => $result->getMessage(),
]);
throw new CrmException('Failed to fetch property');
}
return $result;
}
/**
* @return array<CrmFieldOption>
*/
public function fetchPropertyOptions(string $objectType, string $propertyId): array
{
/** @var array<CrmFieldOption> */
return $this->fetchProperty($objectType, $propertyId)->getOptions();
}
/**
* @return array<array{id:string, label:string, deleted:bool}>
*/
public function fetchCallDispositions(): array
{
/** @var Response $response */
$response = $this->getInstance()->engagements()->getCallDispositions();
/**
* @var array<array{
* id:string,
* label:string,
* deleted: bool
* }>
*/
return $response->toArray();
}
/**
* @return array<CrmFieldOption>
*/
public function fetchOpportunityPipelineStages(): array
{
$stages = [];
$apiResponse = $this->getNewInstance()->crm()->pipelines()->pipelinesApi()->getAll('deals');
if ($apiResponse instanceof Error) {
$this->log->error('[Hubspot] Failed to fetch opportunity pipelines', [
'reason' => $apiResponse->getMessage(),
]);
return [];
}
foreach ($apiResponse->getResults() as $pipeline) {
$pipelineStages = array_map(
static function (PipelineStage $stage) {
return [
'id' => $stage->getId(),
'label' => $stage->getLabel(),
];
},
$pipeline->getStages()
);
$stages = array_merge($stages, $pipelineStages);
}
return $stages;
}
public function fetchOpportunityPipelines(): array
{
$pipelines = [];
try {
$apiResponse = $this->makeRequest('/crm/v3/pipelines/deals');
} catch (\Exception $e) {
$this->log->info('[Hubspot] Failed to fetch opportunity pipelines', [
'reason' => $e->getMessage(),
]);
return [];
}
$response = $apiResponse->toArray();
foreach ($response['results'] as $pipeline) {
$pipelines[] = [
'id' => $pipeline['id'],
'label' => $pipeline['label'],
];
}
return $pipelines;
}
/**
* @return array<CrmFieldOption>
*/
public function fetchMeetingOutcomeFieldOptions(Field $field): array
{
return $field->getCrmProviderId() === 'meetingOutcome'
? $this->fetchMeetingOutcomeTypes()
: $this->fetchCallActivityTypes();
}
public function fetchMeetingOutcomeTypes(): array
{
return $this->extractMeetingTypeOptions(
'[URL_WITH_CREDENTIALS] Response $response */
$response = $this->getInstance()
->getClient()
->request('GET', $endpoint);
/**
* @var array<array{
* value: string,
* label: string,
* displayOrder: int
* }> $optionData
*/
$optionData = $response->toArray()['options'] ?? [];
$options = [];
foreach ($optionData as $item) {
$options[] = [
'id' => $item['value'],
'value' => $item['value'],
'label' => $item['label'],
'display_order' => $item['displayOrder'],
];
}
return $options;
}
/**
* @return array<CrmFieldOption>
*/
public function fetchDispositionFieldOptions(): array
{
$options = [];
$dispositions = $this->fetchCallDispositions();
foreach ($dispositions as $disposition) {
if ($disposition['deleted'] !== false) {
continue;
}
$option['value'] = $disposition['id'];
$option['id'] = $disposition['id'];
$option['label'] = $disposition['label'];
$options[] = $option;
}
return $options;
}
/**
* @return array<CrmFieldOption>
*/
public function fetchOpportunityFieldOptions(Field $field): array
{
if ($field->isStageField()) {
return $this->fetchOpportunityPipelineStages();
}
if ($field->isPipelineField()) {
return $this->fetchOpportunityPipelines();
}
return $this->fetchPropertyOptions('deals', $field->getCrmProviderId());
}
/**
* @throws BadRequest
* @throws HubspotException
*/
public function makeRequest(string $endpoint, $method = 'GET', $payload = [], ?string $queryString = null)
{
$endpoint = self::BASE_URL . $endpoint;
if ($method === 'GET') {
return $this->getInstance()->getClient()?->request(
method: $method,
endpoint: $endpoint,
query_string: $queryString
);
} else {
return $this->getInstance()->getClient()->request($method, $endpoint, [
'json' => ($payload),
]);
}
}
/**
* @throws BadRequest
* @throws HubspotException
*/
public function createMeeting(array $payload): Response
{
$endpoint = '/crm/v3/objects/meetings';
return $this->makeRequest($endpoint, 'POST', $payload);
}
/**
* @throws BadRequest
* @throws HubspotException
*/
public function updateMeeting(string $meetingId, array $payload): Response
{
$endpoint = '/crm/v3/objects/meetings/' . $meetingId;
return $this->makeRequest($endpoint, 'PATCH', $payload);
}
/**
* @throws \Exception
*/
public function createNote(
string $body,
string $ownerId,
int $timestamp,
string $objectId,
NoteObject $noteObject
): ?string {
try {
$noteInput = new SimplePublicObjectInput([
'properties' => [
'hs_note_body' => $body,
'hubspot_owner_id' => $ownerId,
'hs_timestamp' => $timestamp,
],
]);
// Create note
$note = $this->getNewInstance()->crm()->objects()->basicApi()->create('note', $noteInput);
$this->getNewInstance()->crm()->objects()->associationsApi()->create(
'note',
$note->getId(),
$this->getNoteObject($noteObject),
$objectId,
$this->getNoteAssociationType($noteObject),
);
return $note->getId();
} catch (\Exception $e) {
$this->log->error('[Hubspot] Failed to create note', [
'objectId' => $objectId,
'noteObject' => $noteObject->getObjectType(),
'reason' => $e->getMessage(),
]);
\Sentry::captureException($e);
}
return null;
}
public function updateEngagement(string $objectId, array $engagement, array $metadata): void
{
$this->getInstance()->engagements()->update($objectId, $engagement, $metadata);
}
public function getEngagementData(string $engagementId): array
{
$engagement = $this->getInstance()->engagements()->get($engagementId);
return $engagement->toArray();
}
public function createEngagement(array $engagement, array $associations, array $metadata): Response
{
return $this->getInstance()
->engagements()
->create($engagement, $associations, $metadata);
}
public function isUnauthorizedException(\Exception $e): bool
{
// Check for specific HubSpot API exception types first
if ($e instanceof BadRequest) {
// BadRequest can contain 401 status codes
return $e->getCode() === 401;
}
// Check for HTTP client exceptions with status codes
if ($e instanceof \GuzzleHttp\Exception\RequestException && $e->hasResponse()) {
$response = $e->getResponse();
if ($response !== null) {
return $response->getStatusCode() === 401;
}
}
// Check for Guzzle HTTP exceptions
if ($e instanceof \GuzzleHttp\Exception\ClientException) {
return $e->getCode() === 401;
}
// Fallback to string matching as last resort, but be more specific
$message = strtolower($e->getMessage());
return str_contains($message, '401 unauthorized') ||
str_contains($message, 'http 401') ||
str_contains($message, 'status code 401') ||
(preg_match('/\b401\b/', $message) && str_contains($message, 'unauthorized'));
}
/**
* Validates and refreshes the access token if needed before API requests.
* This ensures long-running processes don't fail due to token expiration.
*
* @throws SocialAccountTokenInvalidException
*/
public function ensureValidToken(): void
{
if ($this->oauthAccount === null) {
return;
}
$newToken = $this->tokenManager->ensureValidToken($this->oauthAccount);
if ($newToken !== null) {
$this->accessToken = $newToken;
}
}
public function getConfig()
{
return $this->config;
}
// returns only active (archived=false)
public function getOwners(): array
{
return $this->getNewInstance()->crm()->owners()->getAll();
}
/**
* @param bool $archived
*
* @return array<Owner>|[]
*/
public function getOwnersArchived(bool $archived = true): array
{
$endpoint = '/crm/v3/owners';
$queryParams = [
'archived' => $archived ? 'true' : 'false',
];
$queryString = http_build_query($queryParams);
$owners = [];
try {
$response = $this->makeRequest(endpoint: $endpoint, queryString: $queryString);
$responseData = $response?->toArray();
foreach ($responseData['results'] as $result) {
try {
$owners[] = Owner::create($result);
} catch (Throwable $e) {
$this->log->error('[HubSpot] Failed to process owner data', [
'result' => $result,
'error' => $e->getMessage(),
]);
continue;
}
}
} catch (Throwable $e) {
$this->log->error('HubSpot] Failed to fetch owners', [
'archived' => $archived,
'error' => $e->getMessage(),
]);
return [];
}
return $owners;
}
public function getMeeting(string $engagementId): ObjectWithAssociations
{
return $this->getNewInstance()->crm()->objects()->basicApi()
->getById('meeting', $engagementId, null, 'contact,company,deal');
}
public function deleteEngagement(string $engagementId): void
{
$this->getInstance()->engagements()->delete((int) $engagementId);
}
public function getAssociationsData(array $ids, string $fromObject, string $toObject): array
{
$associationData = [];
$idChunks = array_chunk($ids, self::ASSOCIATIONS_BATCH_SIZE_LIMIT);
foreach ($idChunks as $idChunk) {
try {
$batchInput = new \HubSpot\Client\Crm\Associations\Model\BatchInputPublicObjectId();
$batchInput->setInputs(array_map(function ($id) {
$publicObjectId = new \HubSpot\Client\Crm\Associations\Model\PublicObjectId();
$publicObjectId->setId($id);
return $publicObjectId;
}, $idChunk));
$associatedObjectsData = $this
->getNewInstance()
->crm()
->associations()
->batchApi()
->read($fromObject, $toObject, $batchInput);
if ($associatedObjectsData instanceof \HubSpot\Client\Crm\Associations\Model\BatchResponsePublicAssociationMulti) {
foreach ($associatedObjectsData->getResults() as $association) {
$from = $association->getFrom()->getId();
$toAssociations = $association->getTo();
if (! empty($toAssociations)) {
$associationData[$from] = array_map(function ($item) {
return $item->getId();
}, $toAssociations);
}
}
}
} catch (\Exception $e) {
$this->log->error('[Hubspot] Failed to fetch associations', [
'from_object' => $fromObject,
'to_object' => $toObject,
'reason' => $e->getMessage(),
]);
}
}
return $associationData;
}
/**
* @throws \Exception
*/
private function getNoteAssociationType(NoteObject $noteObject): string
{
return match($noteObject) {
NoteObject::Opportunity => 'note_to_deal',
NoteObject::Lead, NoteObject::Contact => 'note_to_contact', // or 'note_to_lead' if your portal supports it
NoteObject::Account => 'note_to_company',
NoteObject::Call, NoteObject::Event => throw new \Exception('Not supported'),
};
}
/**
* @throws \Exception
*/
private function getNoteObject(NoteObject $noteObject): string
{
return match($noteObject) {
NoteObject::Opportunity => 'deal',
NoteObject::Lead, NoteObject::Contact => 'contact',
NoteObject::Account => 'company',
NoteObject::Call, NoteObject::Event => throw new \Exception('Not supported'),
};
}
public function addAssociations(string $objectType, string $associationType, array $payload): Response
{
$endpoint = "/crm/v4/associations/$objectType/$associationType/batch/create";
return $this->makeRequest($endpoint, 'POST', $payload);
}
public function removeAssociations(string $objectType, string $associationType, array $payload): Response
{
$endpoint = "/crm/v4/associations/$objectType/$associationType/batch/archive";
return $this->makeRequest($endpoint, 'POST', $payload);
}
}
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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
Show more
10:42
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Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
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Retry
You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
Retry
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Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
Copy to clipboard
php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.
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You said: Ok, so what will happen each time I make a request?
You said: Ok, so what will happen each time I make a request?
Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?
13:44
Retry
Edit
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Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Analyzed per-request overhead concerns and quantified Redis performance impact
Analyzed per-request overhead concerns and quantified Redis performance impact
Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out. But the overhead is much smaller than it sounds, and importantly, it's nowhere near your bottleneck.
What "two buckets per request" actually costs
What "two buckets per request" actually costs
Each request makes
one round trip to Redis
— not two — because you fold both bucket checks into a single Lua script that runs atomically server-side:
Copy to clipboard
lua
-- KEYS[1] = burst bucket key (or search)
-- KEYS[2] = daily bucket key
-- ARGV[1] = now (ms) ARGV[2] = window_ms
-- ARGV[3] = bucket_max ARGV[4] = daily_max
-- ARGV[5] = request_id ARGV[6] = daily_ttl
-- Trim sliding window
redis
.
call
(
'ZREMRANGEBYSCORE'
,
KEYS
[
1
]
,
0
,
ARGV
[
1
]
-
ARGV
[
2
]
)
local
burst_used
=
redis
.
call
(
'ZCARD'
,
KEYS
[
1
]
)
local
daily_used
=
tonumber
(
redis
.
call
(
'GET'
,
KEYS
[
2
]
)
or
'0'
)
if
burst_used
>=
tonumber
(
ARGV
[
3
]
)
then
-- Tell caller how long to sleep until oldest entry expires
local
oldest
=
redis
.
call
(
'ZRANGE'
,
KEYS
[
1
]
,
0
,
0
,
'WITHSCORES'
)
return
{
0
,
'BURST'
,
(
oldest
[
2
]
+
ARGV
[
2
]
)
-
ARGV
[
1
]
}
end
if
daily_used
>=
tonumber
(
ARGV
[
4
]
)
then...
|
Claude
|
Claude
|
NULL
|
2634
|
|
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|
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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
Show more
10:42
Retry
Edit
Copy
Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
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You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
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Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
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')
....
|
Claude
|
Claude
|
NULL
|
2635
|
|
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|
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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
Show more
10:42
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Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
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Retry
You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
Retry
Edit
Copy
Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
Copy to clipboard
php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.
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You said: Ok, so what will happen each time I make a request?
You said: Ok, so what will happen each time I make a request?
Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?
13:44
Retry
Edit
Copy
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Analyzed per-request overhead concerns and quantified Redis performance impact
Analyzed per-request overhead concerns and quantified Redis performance impact
Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out. But the overhead is much smaller than it sounds, and importantly, it's nowhere near your bottleneck.
What "two buckets per request" actually costs
What "two buckets per request" actually costs
Each request makes
one round trip to Redis
— not two — because you fold both bucket checks into a single Lua script that runs atomically server-side:
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# [PASSWORD_DOTS] HS [PASSWORD_DOTS]
select * from teams where id = 2; # 2
select * from features; # 2
select * from team_features where team_id = 2; # 2
select * from crm_configurations where id = 2; # 2
select * from users where team_id = 2; #
select * from playbooks where team_id = 2; # event 38
select * from playbook_categories where playbook_id = 38; #
SELECT * FROM activities WHERE crm_configuration_id = 2 and crm_provider_id is not null order by id desc;
[URL_WITH_CREDENTIALS] CrmFieldOption array{id:string, label:string, value?:string}
*/
class Client extends BaseClient implements HubspotClientInterface
{
public const string MIN_API_VERSION = '2';
public const string BASE_URL = '[URL_WITH_CREDENTIALS] T
* @param callable(): T $apiCall
* @return T
*
* @throws RateLimitException
*/
private function executeRequest(callable $apiCall)
{
if (! $this->rateLimiter->canMakeRequest($this->config)) {
$retryAfter = $this->rateLimiter->requestAvailableIn($this->config);
$this->log->warning('[Hubspot] Rate limit exceeded, deferring request', [
'team_id' => $this->config->team_id,
'config_id' => $this->config->getId(),
'retry_after' => $retryAfter,
]);
throw new RateLimitException(
'Hubspot rate limit reached for configuration ' . $this->config->getId(),
$retryAfter,
);
}
$this->rateLimiter->incrementRequestCount($this->config);
try {
return $apiCall();
} catch (Throwable $e) {
if ($this->isHubspotRateLimit($e)) {
$retryAfter = $this->parseRetryAfter($e);
$this->log->warning('[Hubspot] Received 429 from API', [
'team_id' => $this->config->team_id,
'config_id' => $this->config->getId(),
'retry_after' => $retryAfter,
'reason' => $e->getMessage(),
]);
throw new RateLimitException('Hubspot returned 429', $retryAfter, $e);
}
throw $e;
}
}
private function isHubspotRateLimit(Throwable $e): bool
{
return method_exists($e, 'getCode') && (int) $e->getCode() === 429;
}
private function parseRetryAfter(Throwable $e): int
{
if (method_exists($e, 'getResponseHeaders')) {
$headers = $e->getResponseHeaders() ?: [];
$value = $headers['Retry-After'] ?? $headers['retry-after'] ?? null;
if (is_array($value)) {
$value = $value[0] ?? null;
}
if (is_numeric($value)) {
return (int) $value;
}
}
return 10;
}
public function getMinimumApiVersion(): string
{
return self::MIN_API_VERSION;
}
public function getInstance(): Factory
{
return new Factory([
'key' => $this->accessToken,
'oauth2' => true,
'base_url' => $this->baseUrl,
]);
}
public function getNewInstance(): Discovery
{
return \HubSpot\Factory::createWithAccessToken($this->accessToken);
}
/**
* Secondly and daily limits for Hubspot API
*
* Product Tier: Free & Starter | Professional & Enterprise | API add-on (any tier)
* Burst: 100/10 seconds | 150/10 seconds | 200/10 seconds
* Daily: 250,000 | 500,000 | 1,000,000
*
* Official documentation states: The search endpoints are rate limited to five requests per second.
* Since with 5 RPS were still hitting secondly rate limits we lowered it to 4
*/
public function getPaginatedData(array $payload, string $type, int $offset = 0): array
{
$total = 0;
$lastId = null;
$rows = [];
foreach ($this->getPaginatedDataGenerator($payload, $type, $offset, $total, $lastId) as $row) {
$rows[] = $row;
}
return ['results' => $rows, 'total' => $total, 'last_record' => $lastId];
}
/**
* @throws HubspotException
* @throws SocialAccountTokenInvalidException
* @throws BadRequest
*/
public function getPaginatedDataGenerator(
array $payload,
string $type,
int $offset = 0,
int &$total = 0,
?string &$lastRecordId = null
): \Generator {
return $this->paginationService->getPaginatedDataGenerator(
$this,
$payload,
$type,
$offset,
$total,
$lastRecordId
);
}
/**
* @throws DealApiException
* @throws CrmException
*/
public function getOpportunityById(string $crmId, array $fields): array
{
try {
$deal = $this->executeRequest(fn () => $this->getNewInstance()->crm()->deals()->basicApi()->getById(
$crmId,
implode(',', $fields),
'companies,contacts'
));
} catch (DealApiException $e) {
$this->log->info('[Hubspot] Failed to fetch opportunity', [
'crm_id' => $crmId,
'reason' => $e->getMessage(),
]);
throw $e;
}
if (! $deal instanceof DealWithAssociations) {
throw new CrmException('Deal not found');
}
return [
'id' => $deal->getId(),
'properties' => $deal->getProperties(),
'associations' => $deal->getAssociations(),
];
}
/**
* Generic batch read method for HubSpot objects
*
* @param string $objectType The object type ('deals', 'companies', 'contacts')
* @param array<string> $crmIds Array of HubSpot object IDs (max 100)
* @param array<string> $fields Array of property names to fetch
*
* @return array<string, array> Array keyed by CRM ID with object data
*/
private function batchReadObjects(string $objectType, array $crmIds, array $fields): array
{
if (empty($crmIds)) {
return [];
}
$this->validateBatchSize($objectType, $crmIds);
$this->ensureValidToken();
try {
$batchConfig = $this->createBatchConfiguration($objectType);
$batchReadRequest = $this->prepareBatchRequest($batchConfig, $crmIds, $fields);
$response = $batchConfig['api']->read($batchReadRequest);
$this->validateApiResponse($response, $objectType);
$results = $this->processApiResults($response);
$this->logBatchResults($objectType, $crmIds, $results);
return $results;
} catch (\Throwable $e) {
$this->handleBatchError($e, $objectType, $crmIds);
}
}
private function validateBatchSize(string $objectType, array $crmIds): void
{
if (count($crmIds) > 100) {
throw new \InvalidArgumentException("Batch size cannot exceed 100 {$objectType}");
}
}
private function createBatchConfiguration(string $objectType): array
{
$configurations = [
'deals' => [
'batchReadRequest' => new \HubSpot\Client\Crm\Deals\Model\BatchReadInputSimplePublicObjectId(),
'inputClass' => \HubSpot\Client\Crm\Deals\Model\SimplePublicObjectId::class,
'api' => $this->getNewInstance()->crm()->deals()->batchApi(),
],
'companies' => [
'batchReadRequest' => new \HubSpot\Client\Crm\Companies\Model\BatchReadInputSimplePublicObjectId(),
'inputClass' => \HubSpot\Client\Crm\Companies\Model\SimplePublicObjectId::class,
'api' => $this->getNewInstance()->crm()->companies()->batchApi(),
],
'contacts' => [
'batchReadRequest' => new \HubSpot\Client\Crm\Contacts\Model\BatchReadInputSimplePublicObjectId(),
'inputClass' => \HubSpot\Client\Crm\Contacts\Model\SimplePublicObjectId::class,
'api' => $this->getNewInstance()->crm()->contacts()->batchApi(),
],
];
if (! isset($configurations[$objectType])) {
throw new \InvalidArgumentException("Unsupported object type: {$objectType}");
}
return $configurations[$objectType];
}
private function prepareBatchRequest(array $batchConfig, array $crmIds, array $fields): object
{
$batchReadRequest = $batchConfig['batchReadRequest'];
$inputClass = $batchConfig['inputClass'];
$inputs = array_map(function ($crmId) use ($inputClass) {
$input = new $inputClass();
$input->setId($crmId);
return $input;
}, $crmIds);
$batchReadRequest->setInputs($inputs);
$batchReadRequest->setProperties($fields);
return $batchReadRequest;
}
private function validateApiResponse($response, string $objectType): void
{
if (! $response) {
throw new CrmException("HubSpot API returned null response for {$objectType} batch read");
}
}
private function processApiResults($response): array
{
$results = [];
$responseResults = $response->getResults();
if ($responseResults) {
foreach ($responseResults as $object) {
if ($object && $object->getId()) {
$results[$object->getId()] = [
'id' => $object->getId(),
'properties' => $object->getProperties() ?: [],
];
}
}
}
return $results;
}
private function logBatchResults(string $objectType, array $crmIds, array $results): void
{
$this->log->info("[HubSpot] Batch fetched {$objectType}", [
'requested_count' => count($crmIds),
'returned_count' => count($results),
'crm_ids' => $crmIds,
]);
}
private function handleBatchError(\Throwable $e, string $objectType, array $crmIds): void
{
$errorMessage = $e->getMessage() ?: 'Unknown error';
$errorTrace = $e->getTraceAsString() ?: 'No trace available';
$this->log->error("[HubSpot] Failed to batch fetch {$objectType}", [
'crm_ids' => $crmIds,
'error' => $errorMessage,
'trace' => $errorTrace,
]);
throw new CrmException("Failed to batch fetch {$objectType}: " . $errorMessage);
}
/**
* Batch read multiple opportunities by their CRM IDs
*
* @param array<string> $crmIds Array of HubSpot deal IDs (max 100)
* @param array<string> $fields Array of property names to fetch
*
* @return array<string, array> Array keyed by CRM ID with opportunity data
*/
public function getOpportunitiesByIds(array $crmIds, array $fields): array
{
return $this->batchReadObjects('deals', $crmIds, $fields);
}
/**
* Batch read multiple companies by their CRM IDs
*
* @param array<string> $crmIds Array of HubSpot company IDs (max 100)
* @param array<string> $fields Array of property names to fetch
*
* @return array<string, array> Array keyed by CRM ID with company data
*/
public function getCompaniesByIds(array $crmIds, array $fields): array
{
return $this->batchReadObjects('companies', $crmIds, $fields);
}
/**
* Batch read multiple contacts by their CRM IDs
*
* @param array<string> $crmIds Array of HubSpot contact IDs (max 100)
* @param array<string> $fields Array of property names to fetch
*
* @return array<string, array> Array keyed by CRM ID with contact data
*/
public function getContactsByIds(array $crmIds, array $fields): array
{
return $this->batchReadObjects('contacts', $crmIds, $fields);
}
/**
* @throws CompanyApiException
* @throws CrmException
*/
public function getAccountById(string $crmId, array $fields): array
{
try {
$company = $this->getNewInstance()->crm()->companies()->basicApi()->getById(
$crmId,
implode(',', $fields),
);
} catch (CompanyApiException $e) {
$this->log->info('[Hubspot] Failed to fetch account', [
'crm_id' => $crmId,
'reason' => $e->getMessage(),
]);
throw $e;
}
if (! $company instanceof CompaniesWithAssociations) {
throw new CrmException('Account not found');
}
return [
'id' => $company->getId(),
'properties' => $company->getProperties(),
];
}
/**
* @throws ContactApiException
* @throws CrmException
*/
public function getContactById(string $crmId, array $fields): array
{
try {
$contact = $this->getNewInstance()->crm()->contacts()->basicApi()->getById(
$crmId,
implode(',', $fields)
);
} catch (ContactApiException $e) {
$this->log->info('[Hubspot] Failed to fetch contact', [
'crm_id' => $crmId,
'reason' => $e->getMessage(),
]);
throw $e;
}
if (! $contact instanceof ContactsWithAssociations) {
throw new CrmException('Contact not found');
}
return [
'id' => $contact->getId(),
'properties' => $contact->getProperties(),
];
}
/**
* This is email search request that Hubspot offers as GET (more generous quota)
*/
public function getContactByEmail(string $email, array $fields = []): array
{
try {
$contact = $this->getNewInstance()->crm()->contacts()->basicApi()->getById(
$email,
implode(',', $fields),
null,
false,
'email'
);
return [
'id' => $contact->getId(),
'properties' => $contact->getProperties(),
];
} catch (ContactApiException $e) {
$this->log->info('[Hubspot] Failed to fetch contact', [
'email' => $email,
'reason' => $e->getMessage(),
]);
return [];
}
}
/**
* @throws CrmException
*/
public function fetchProperty(string $objectType, string $propertyId): Property
{
$result = $this->getNewInstance()->crm()->properties()->coreApi()->getByName($objectType, $propertyId);
if (! $result instanceof Property) {
$this->log->error('[Hubspot] Failed to fetch property', [
'object_type' => $objectType,
'property_id' => $propertyId,
'reason' => $result->getMessage(),
]);
throw new CrmException('Failed to fetch property');
}
return $result;
}
/**
* @return array<CrmFieldOption>
*/
public function fetchPropertyOptions(string $objectType, string $propertyId): array
{
/** @var array<CrmFieldOption> */
return $this->fetchProperty($objectType, $propertyId)->getOptions();
}
/**
* @return array<array{id:string, label:string, deleted:bool}>
*/
public function fetchCallDispositions(): array
{
/** @var Response $response */
$response = $this->getInstance()->engagements()->getCallDispositions();
/**
* @var array<array{
* id:string,
* label:string,
* deleted: bool
* }>
*/
return $response->toArray();
}
/**
* @return array<CrmFieldOption>
*/
public function fetchOpportunityPipelineStages(): array
{
$stages = [];
$apiResponse = $this->getNewInstance()->crm()->pipelines()->pipelinesApi()->getAll('deals');
if ($apiResponse instanceof Error) {
$this->log->error('[Hubspot] Failed to fetch opportunity pipelines', [
'reason' => $apiResponse->getMessage(),
]);
return [];
}
foreach ($apiResponse->getResults() as $pipeline) {
$pipelineStages = array_map(
static function (PipelineStage $stage) {
return [
'id' => $stage->getId(),
'label' => $stage->getLabel(),
];
},
$pipeline->getStages()
);
$stages = array_merge($stages, $pipelineStages);
}
return $stages;
}
public function fetchOpportunityPipelines(): array
{
$pipelines = [];
try {
$apiResponse = $this->makeRequest('/crm/v3/pipelines/deals');
} catch (\Exception $e) {
$this->log->info('[Hubspot] Failed to fetch opportunity pipelines', [
'reason' => $e->getMessage(),
]);
return [];
}
$response = $apiResponse->toArray();
foreach ($response['results'] as $pipeline) {
$pipelines[] = [
'id' => $pipeline['id'],
'label' => $pipeline['label'],
];
}
return $pipelines;
}
/**
* @return array<CrmFieldOption>
*/
public function fetchMeetingOutcomeFieldOptions(Field $field): array
{
return $field->getCrmProviderId() === 'meetingOutcome'
? $this->fetchMeetingOutcomeTypes()
: $this->fetchCallActivityTypes();
}
public function fetchMeetingOutcomeTypes(): array
{
return $this->extractMeetingTypeOptions(
'[URL_WITH_CREDENTIALS] Response $response */
$response = $this->getInstance()
->getClient()
->request('GET', $endpoint);
/**
* @var array<array{
* value: string,
* label: string,
* displayOrder: int
* }> $optionData
*/
$optionData = $response->toArray()['options'] ?? [];
$options = [];
foreach ($optionData as $item) {
$options[] = [
'id' => $item['value'],
'value' => $item['value'],
'label' => $item['label'],
'display_order' => $item['displayOrder'],
];
}
return $options;
}
/**
* @return array<CrmFieldOption>
*/
public function fetchDispositionFieldOptions(): array
{
$options = [];
$dispositions = $this->fetchCallDispositions();
foreach ($dispositions as $disposition) {
if ($disposition['deleted'] !== false) {
continue;
}
$option['value'] = $disposition['id'];
$option['id'] = $disposition['id'];
$option['label'] = $disposition['label'];
$options[] = $option;
}
return $options;
}
/**
* @return array<CrmFieldOption>
*/
public function fetchOpportunityFieldOptions(Field $field): array
{
if ($field->isStageField()) {
return $this->fetchOpportunityPipelineStages();
}
if ($field->isPipelineField()) {
return $this->fetchOpportunityPipelines();
}
return $this->fetchPropertyOptions('deals', $field->getCrmProviderId());
}
/**
* @throws BadRequest
* @throws HubspotException
*/
public function makeRequest(string $endpoint, $method = 'GET', $payload = [], ?string $queryString = null)
{
$endpoint = self::BASE_URL . $endpoint;
if ($method === 'GET') {
return $this->getInstance()->getClient()?->request(
method: $method,
endpoint: $endpoint,
query_string: $queryString
);
} else {
return $this->getInstance()->getClient()->request($method, $endpoint, [
'json' => ($payload),
]);
}
}
/**
* @throws BadRequest
* @throws HubspotException
*/
public function createMeeting(array $payload): Response
{
$endpoint = '/crm/v3/objects/meetings';
return $this->makeRequest($endpoint, 'POST', $payload);
}
/**
* @throws BadRequest
* @throws HubspotException
*/
public function updateMeeting(string $meetingId, array $payload): Response
{
$endpoint = '/crm/v3/objects/meetings/' . $meetingId;
return $this->makeRequest($endpoint, 'PATCH', $payload);
}
/**
* @throws \Exception
*/
public function createNote(
string $body,
string $ownerId,
int $timestamp,
string $objectId,
NoteObject $noteObject
): ?string {
try {
$noteInput = new SimplePublicObjectInput([
'properties' => [
'hs_note_body' => $body,
'hubspot_owner_id' => $ownerId,
'hs_timestamp' => $timestamp,
],
]);
// Create note
$note = $this->getNewInstance()->crm()->objects()->basicApi()->create('note', $noteInput);
$this->getNewInstance()->crm()->objects()->associationsApi()->create(
'note',
$note->getId(),
$this->getNoteObject($noteObject),
$objectId,
$this->getNoteAssociationType($noteObject),
);
return $note->getId();
} catch (\Exception $e) {
$this->log->error('[Hubspot] Failed to create note', [
'objectId' => $objectId,
'noteObject' => $noteObject->getObjectType(),
'reason' => $e->getMessage(),
]);
\Sentry::captureException($e);
}
return null;
}
public function updateEngagement(string $objectId, array $engagement, array $metadata): void
{
$this->getInstance()->engagements()->update($objectId, $engagement, $metadata);
}
public function getEngagementData(string $engagementId): array
{
$engagement = $this->getInstance()->engagements()->get($engagementId);
return $engagement->toArray();
}
public function createEngagement(array $engagement, array $associations, array $metadata): Response
{
return $this->getInstance()
->engagements()
->create($engagement, $associations, $metadata);
}
public function isUnauthorizedException(\Exception $e): bool
{
// Check for specific HubSpot API exception types first
if ($e instanceof BadRequest) {
// BadRequest can contain 401 status codes
return $e->getCode() === 401;
}
// Check for HTTP client exceptions with status codes
if ($e instanceof \GuzzleHttp\Exception\RequestException && $e->hasResponse()) {
$response = $e->getResponse();
if ($response !== null) {
return $response->getStatusCode() === 401;
}
}
// Check for Guzzle HTTP exceptions
if ($e instanceof \GuzzleHttp\Exception\ClientException) {
return $e->getCode() === 401;
}
// Fallback to string matching as last resort, but be more specific
$message = strtolower($e->getMessage());
return str_contains($message, '401 unauthorized') ||
str_contains($message, 'http 401') ||
str_contains($message, 'status code 401') ||
(preg_match('/\b401\b/', $message) && str_contains($message, 'unauthorized'));
}
/**
* Validates and refreshes the access token if needed before API requests.
* This ensures long-running processes don't fail due to token expiration.
*
* @throws SocialAccountTokenInvalidException
*/
public function ensureValidToken(): void
{
if ($this->oauthAccount === null) {
return;
}
$newToken = $this->tokenManager->ensureValidToken($this->oauthAccount);
if ($newToken !== null) {
$this->accessToken = $newToken;
}
}
public function getConfig()
{
return $this->config;
}
// returns only active (archived=false)
public function getOwners(): array
{
return $this->getNewInstance()->crm()->owners()->getAll();
}
/**
* @param bool $archived
*
* @return array<Owner>|[]
*/
public function getOwnersArchived(bool $archived = true): array
{
$endpoint = '/crm/v3/owners';
$queryParams = [
'archived' => $archived ? 'true' : 'false',
];
$queryString = http_build_query($queryParams);
$owners = [];
try {
$response = $this->makeRequest(endpoint: $endpoint, queryString: $queryString);
$responseData = $response?->toArray();
foreach ($responseData['results'] as $result) {
try {
$owners[] = Owner::create($result);
} catch (Throwable $e) {
$this->log->error('[HubSpot] Failed to process owner data', [
'result' => $result,
'error' => $e->getMessage(),
]);
continue;
}
}
} catch (Throwable $e) {
$this->log->error('HubSpot] Failed to fetch owners', [
'archived' => $archived,
'error' => $e->getMessage(),
]);
return [];
}
return $owners;
}
public function getMeeting(string $engagementId): ObjectWithAssociations
{
return $this->getNewInstance()->crm()->objects()->basicApi()
->getById('meeting', $engagementId, null, 'contact,company,deal');
}
public function deleteEngagement(string $engagementId): void
{
$this->getInstance()->engagements()->delete((int) $engagementId);
}
public function getAssociationsData(array $ids, string $fromObject, string $toObject): array
{
$associationData = [];
$idChunks = array_chunk($ids, self::ASSOCIATIONS_BATCH_SIZE_LIMIT);
foreach ($idChunks as $idChunk) {
try {
$batchInput = new \HubSpot\Client\Crm\Associations\Model\BatchInputPublicObjectId();
$batchInput->setInputs(array_map(function ($id) {
$publicObjectId = new \HubSpot\Client\Crm\Associations\Model\PublicObjectId();
$publicObjectId->setId($id);
return $publicObjectId;
}, $idChunk));
$associatedObjectsData = $this
->getNewInstance()
->crm()
->associations()
->batchApi()
->read($fromObject, $toObject, $batchInput);
if ($associatedObjectsData instanceof \HubSpot\Client\Crm\Associations\Model\BatchResponsePublicAssociationMulti) {
foreach ($associatedObjectsData->getResults() as $association) {
$from = $association->getFrom()->getId();
$toAssociations = $association->getTo();
if (! empty($toAssociations)) {
$associationData[$from] = array_map(function ($item) {
return $item->getId();
}, $toAssociations);
}
}
}
} catch (\Exception $e) {
$this->log->error('[Hubspot] Failed to fetch associations', [
'from_object' => $fromObject,
'to_object' => $toObject,
'reason' => $e->getMessage(),
]);
}
}
return $associationData;
}
/**
* @throws \Exception
*/
private function getNoteAssociationType(NoteObject $noteObject): string
{
return match($noteObject) {
NoteObject::Opportunity => 'note_to_deal',
NoteObject::Lead, NoteObject::Contact => 'note_to_contact', // or 'note_to_lead' if your portal supports it
NoteObject::Account => 'note_to_company',
NoteObject::Call, NoteObject::Event => throw new \Exception('Not supported'),
};
}
/**
* @throws \Exception
*/
private function getNoteObject(NoteObject $noteObject): string
{
return match($noteObject) {
NoteObject::Opportunity => 'deal',
NoteObject::Lead, NoteObject::Contact => 'contact',
NoteObject::Account => 'company',
NoteObject::Call, NoteObject::Event => throw new \Exception('Not supported'),
};
}
public function addAssociations(string $objectType, string $associationType, array $payload): Response
{
$endpoint = "/crm/v4/associations/$objectType/$associationType/batch/create";
return $this->makeRequest($endpoint, 'POST', $payload);
}
public function removeAssociations(string $objectType, string $associationType, array $payload): Response
{
$endpoint = "/crm/v4/associations/$objectType/$associationType/batch/archive";
return $this->makeRequest($endpoint, 'POST', $payload);
}
}
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# [PASSWORD_DOTS] HS [PASSWORD_DOTS]
select * from teams where id = 2; # 2
select * from features; # 2
select * from team_features where team_id = 2; # 2
select * from crm_configurations where id = 2; # 2
select * from users where team_id = 2; #
select * from playbooks where team_id = 2; # event 38
select * from playbook_categories where playbook_id = 38; #
SELECT * FROM activities WHERE crm_configuration_id = 2 and crm_provider_id is not null order by id desc;
[URL_WITH_CREDENTIALS] CrmFieldOption array{id:string, label:string, value?:string}
*/
class Client extends BaseClient implements HubspotClientInterface
{
public const string MIN_API_VERSION = '2';
public const string BASE_URL = '[URL_WITH_CREDENTIALS] T
* @param callable(): T $apiCall
* @return T
*
* @throws RateLimitException
*/
private function executeRequest(callable $apiCall)
{
if (! $this->rateLimiter->canMakeRequest($this->config)) {
$retryAfter = $this->rateLimiter->requestAvailableIn($this->config);
$this->log->warning('[Hubspot] Rate limit exceeded, deferring request', [
'team_id' => $this->config->team_id,
'config_id' => $this->config->getId(),
'retry_after' => $retryAfter,
]);
throw new RateLimitException(
'Hubspot rate limit reached for configuration ' . $this->config->getId(),
$retryAfter,
);
}
$this->rateLimiter->incrementRequestCount($this->config);
try {
return $apiCall();
} catch (Throwable $e) {
if ($this->isHubspotRateLimit($e)) {
$retryAfter = $this->parseRetryAfter($e);
$this->log->warning('[Hubspot] Received 429 from API', [
'team_id' => $this->config->team_id,
'config_id' => $this->config->getId(),
'retry_after' => $retryAfter,
'reason' => $e->getMessage(),
]);
throw new RateLimitException('Hubspot returned 429', $retryAfter, $e);
}
throw $e;
}
}
private function isHubspotRateLimit(Throwable $e): bool
{
return method_exists($e, 'getCode') && (int) $e->getCode() === 429;
}
private function parseRetryAfter(Throwable $e): int
{
if (method_exists($e, 'getResponseHeaders')) {
$headers = $e->getResponseHeaders() ?: [];
$value = $headers['Retry-After'] ?? $headers['retry-after'] ?? null;
if (is_array($value)) {
$value = $value[0] ?? null;
}
if (is_numeric($value)) {
return (int) $value;
}
}
return 10;
}
public function getMinimumApiVersion(): string
{
return self::MIN_API_VERSION;
}
public function getInstance(): Factory
{
return new Factory([
'key' => $this->accessToken,
'oauth2' => true,
'base_url' => $this->baseUrl,
]);
}
public function getNewInstance(): Discovery
{
return \HubSpot\Factory::createWithAccessToken($this->accessToken);
}
/**
* Secondly and daily limits for Hubspot API
*
* Product Tier: Free & Starter | Professional & Enterprise | API add-on (any tier)
* Burst: 100/10 seconds | 150/10 seconds | 200/10 seconds
* Daily: 250,000 | 500,000 | 1,000,000
*
* Official documentation states: The search endpoints are rate limited to five requests per second.
* Since with 5 RPS were still hitting secondly rate limits we lowered it to 4
*/
public function getPaginatedData(array $payload, string $type, int $offset = 0): array
{
$total = 0;
$lastId = null;
$rows = [];
foreach ($this->getPaginatedDataGenerator($payload, $type, $offset, $total, $lastId) as $row) {
$rows[] = $row;
}
return ['results' => $rows, 'total' => $total, 'last_record' => $lastId];
}
/**
* @throws HubspotException
* @throws SocialAccountTokenInvalidException
* @throws BadRequest
*/
public function getPaginatedDataGenerator(
array $payload,
string $type,
int $offset = 0,
int &$total = 0,
?string &$lastRecordId = null
): \Generator {
return $this->paginationService->getPaginatedDataGenerator(
$this,
$payload,
$type,
$offset,
$total,
$lastRecordId
);
}
/**
* @throws DealApiException
* @throws CrmException
*/
public function getOpportunityById(string $crmId, array $fields): array
{
try {
$deal = $this->executeRequest(fn () => $this->getNewInstance()->crm()->deals()->basicApi()->getById(
$crmId,
implode(',', $fields),
'companies,contacts'
));
} catch (DealApiException $e) {
$this->log->info('[Hubspot] Failed to fetch opportunity', [
'crm_id' => $crmId,
'reason' => $e->getMessage(),
]);
throw $e;
}
if (! $deal instanceof DealWithAssociations) {
throw new CrmException('Deal not found');
}
return [
'id' => $deal->getId(),
'properties' => $deal->getProperties(),
'associations' => $deal->getAssociations(),
];
}
/**
* Generic batch read method for HubSpot objects
*
* @param string $objectType The object type ('deals', 'companies', 'contacts')
* @param array<string> $crmIds Array of HubSpot object IDs (max 100)
* @param array<string> $fields Array of property names to fetch
*
* @return array<string, array> Array keyed by CRM ID with object data
*/
private function batchReadObjects(string $objectType, array $crmIds, array $fields): array
{
if (empty($crmIds)) {
return [];
}
$this->validateBatchSize($objectType, $crmIds);
$this->ensureValidToken();
try {
$batchConfig = $this->createBatchConfiguration($objectType);
$batchReadRequest = $this->prepareBatchRequest($batchConfig, $crmIds, $fields);
$response = $batchConfig['api']->read($batchReadRequest);
$this->validateApiResponse($response, $objectType);
$results = $this->processApiResults($response);
$this->logBatchResults($objectType, $crmIds, $results);
return $results;
} catch (\Throwable $e) {
$this->handleBatchError($e, $objectType, $crmIds);
}
}
private function validateBatchSize(string $objectType, array $crmIds): void
{
if (count($crmIds) > 100) {
throw new \InvalidArgumentException("Batch size cannot exceed 100 {$objectType}");
}
}
private function createBatchConfiguration(string $objectType): array
{
$configurations = [
'deals' => [
'batchReadRequest' => new \HubSpot\Client\Crm\Deals\Model\BatchReadInputSimplePublicObjectId(),
'inputClass' => \HubSpot\Client\Crm\Deals\Model\SimplePublicObjectId::class,
'api' => $this->getNewInstance()->crm()->deals()->batchApi(),
],
'companies' => [
'batchReadRequest' => new \HubSpot\Client\Crm\Companies\Model\BatchReadInputSimplePublicObjectId(),
'inputClass' => \HubSpot\Client\Crm\Companies\Model\SimplePublicObjectId::class,
'api' => $this->getNewInstance()->crm()->companies()->batchApi(),
],
'contacts' => [
'batchReadRequest' => new \HubSpot\Client\Crm\Contacts\Model\BatchReadInputSimplePublicObjectId(),
'inputClass' => \HubSpot\Client\Crm\Contacts\Model\SimplePublicObjectId::class,
'api' => $this->getNewInstance()->crm()->contacts()->batchApi(),
],
];
if (! isset($configurations[$objectType])) {
throw new \InvalidArgumentException("Unsupported object type: {$objectType}");
}
return $configurations[$objectType];
}
private function prepareBatchRequest(array $batchConfig, array $crmIds, array $fields): object
{
$batchReadRequest = $batchConfig['batchReadRequest'];
$inputClass = $batchConfig['inputClass'];
$inputs = array_map(function ($crmId) use ($inputClass) {
$input = new $inputClass();
$input->setId($crmId);
return $input;
}, $crmIds);
$batchReadRequest->setInputs($inputs);
$batchReadRequest->setProperties($fields);
return $batchReadRequest;
}
private function validateApiResponse($response, string $objectType): void
{
if (! $response) {
throw new CrmException("HubSpot API returned null response for {$objectType} batch read");
}
}
private function processApiResults($response): array
{
$results = [];
$responseResults = $response->getResults();
if ($responseResults) {
foreach ($responseResults as $object) {
if ($object && $object->getId()) {
$results[$object->getId()] = [
'id' => $object->getId(),
'properties' => $object->getProperties() ?: [],
];
}
}
}
return $results;
}
private function logBatchResults(string $objectType, array $crmIds, array $results): void
{
$this->log->info("[HubSpot] Batch fetched {$objectType}", [
'requested_count' => count($crmIds),
'returned_count' => count($results),
'crm_ids' => $crmIds,
]);
}
private function handleBatchError(\Throwable $e, string $objectType, array $crmIds): void
{
$errorMessage = $e->getMessage() ?: 'Unknown error';
$errorTrace = $e->getTraceAsString() ?: 'No trace available';
$this->log->error("[HubSpot] Failed to batch fetch {$objectType}", [
'crm_ids' => $crmIds,
'error' => $errorMessage,
'trace' => $errorTrace,
]);
throw new CrmException("Failed to batch fetch {$objectType}: " . $errorMessage);
}
/**
* Batch read multiple opportunities by their CRM IDs
*
* @param array<string> $crmIds Array of HubSpot deal IDs (max 100)
* @param array<string> $fields Array of property names to fetch
*
* @return array<string, array> Array keyed by CRM ID with opportunity data
*/
public function getOpportunitiesByIds(array $crmIds, array $fields): array
{
return $this->batchReadObjects('deals', $crmIds, $fields);
}
/**
* Batch read multiple companies by their CRM IDs
*
* @param array<string> $crmIds Array of HubSpot company IDs (max 100)
* @param array<string> $fields Array of property names to fetch
*
* @return array<string, array> Array keyed by CRM ID with company data
*/
public function getCompaniesByIds(array $crmIds, array $fields): array
{
return $this->batchReadObjects('companies', $crmIds, $fields);
}
/**
* Batch read multiple contacts by their CRM IDs
*
* @param array<string> $crmIds Array of HubSpot contact IDs (max 100)
* @param array<string> $fields Array of property names to fetch
*
* @return array<string, array> Array keyed by CRM ID with contact data
*/
public function getContactsByIds(array $crmIds, array $fields): array
{
return $this->batchReadObjects('contacts', $crmIds, $fields);
}
/**
* @throws CompanyApiException
* @throws CrmException
*/
public function getAccountById(string $crmId, array $fields): array
{
try {
$company = $this->getNewInstance()->crm()->companies()->basicApi()->getById(
$crmId,
implode(',', $fields),
);
} catch (CompanyApiException $e) {
$this->log->info('[Hubspot] Failed to fetch account', [
'crm_id' => $crmId,
'reason' => $e->getMessage(),
]);
throw $e;
}
if (! $company instanceof CompaniesWithAssociations) {
throw new CrmException('Account not found');
}
return [
'id' => $company->getId(),
'properties' => $company->getProperties(),
];
}
/**
* @throws ContactApiException
* @throws CrmException
*/
public function getContactById(string $crmId, array $fields): array
{
try {
$contact = $this->getNewInstance()->crm()->contacts()->basicApi()->getById(
$crmId,
implode(',', $fields)
);
} catch (ContactApiException $e) {
$this->log->info('[Hubspot] Failed to fetch contact', [
'crm_id' => $crmId,
'reason' => $e->getMessage(),
]);
throw $e;
}
if (! $contact instanceof ContactsWithAssociations) {
throw new CrmException('Contact not found');
}
return [
'id' => $contact->getId(),
'properties' => $contact->getProperties(),
];
}
/**
* This is email search request that Hubspot offers as GET (more generous quota)
*/
public function getContactByEmail(string $email, array $fields = []): array
{
try {
$contact = $this->getNewInstance()->crm()->contacts()->basicApi()->getById(
$email,
implode(',', $fields),
null,
false,
'email'
);
return [
'id' => $contact->getId(),
'properties' => $contact->getProperties(),
];
} catch (ContactApiException $e) {
$this->log->info('[Hubspot] Failed to fetch contact', [
'email' => $email,
'reason' => $e->getMessage(),
]);
return [];
}
}
/**
* @throws CrmException
*/
public function fetchProperty(string $objectType, string $propertyId): Property
{
$result = $this->getNewInstance()->crm()->properties()->coreApi()->getByName($objectType, $propertyId);
if (! $result instanceof Property) {
$this->log->error('[Hubspot] Failed to fetch property', [
'object_type' => $objectType,
'property_id' => $propertyId,
'reason' => $result->getMessage(),
]);
throw new CrmException('Failed to fetch property');
}
return $result;
}
/**
* @return array<CrmFieldOption>
*/
public function fetchPropertyOptions(string $objectType, string $propertyId): array
{
/** @var array<CrmFieldOption> */
return $this->fetchProperty($objectType, $propertyId)->getOptions();
}
/**
* @return array<array{id:string, label:string, deleted:bool}>
*/
public function fetchCallDispositions(): array
{
/** @var Response $response */
$response = $this->getInstance()->engagements()->getCallDispositions();
/**
* @var array<array{
* id:string,
* label:string,
* deleted: bool
* }>
*/
return $response->toArray();
}
/**
* @return array<CrmFieldOption>
*/
public function fetchOpportunityPipelineStages(): array
{
$stages = [];
$apiResponse = $this->getNewInstance()->crm()->pipelines()->pipelinesApi()->getAll('deals');
if ($apiResponse instanceof Error) {
$this->log->error('[Hubspot] Failed to fetch opportunity pipelines', [
'reason' => $apiResponse->getMessage(),
]);
return [];
}
foreach ($apiResponse->getResults() as $pipeline) {
$pipelineStages = array_map(
static function (PipelineStage $stage) {
return [
'id' => $stage->getId(),
'label' => $stage->getLabel(),
];
},
$pipeline->getStages()
);
$stages = array_merge($stages, $pipelineStages);
}
return $stages;
}
public function fetchOpportunityPipelines(): array
{
$pipelines = [];
try {
$apiResponse = $this->makeRequest('/crm/v3/pipelines/deals');
} catch (\Exception $e) {
$this->log->info('[Hubspot] Failed to fetch opportunity pipelines', [
'reason' => $e->getMessage(),
]);
return [];
}
$response = $apiResponse->toArray();
foreach ($response['results'] as $pipeline) {
$pipelines[] = [
'id' => $pipeline['id'],
'label' => $pipeline['label'],
];
}
return $pipelines;
}
/**
* @return array<CrmFieldOption>
*/
public function fetchMeetingOutcomeFieldOptions(Field $field): array
{
return $field->getCrmProviderId() === 'meetingOutcome'
? $this->fetchMeetingOutcomeTypes()
: $this->fetchCallActivityTypes();
}
public function fetchMeetingOutcomeTypes(): array
{
return $this->extractMeetingTypeOptions(
'[URL_WITH_CREDENTIALS] Response $response */
$response = $this->getInstance()
->getClient()
->request('GET', $endpoint);
/**
* @var array<array{
* value: string,
* label: string,
* displayOrder: int
* }> $optionData
*/
$optionData = $response->toArray()['options'] ?? [];
$options = [];
foreach ($optionData as $item) {
$options[] = [
'id' => $item['value'],
'value' => $item['value'],
'label' => $item['label'],
'display_order' => $item['displayOrder'],
];
}
return $options;
}
/**
* @return array<CrmFieldOption>
*/
public function fetchDispositionFieldOptions(): array
{
$options = [];
$dispositions = $this->fetchCallDispositions();
foreach ($dispositions as $disposition) {
if ($disposition['deleted'] !== false) {
continue;
}
$option['value'] = $disposition['id'];
$option['id'] = $disposition['id'];
$option['label'] = $disposition['label'];
$options[] = $option;
}
return $options;
}
/**
* @return array<CrmFieldOption>
*/
public function fetchOpportunityFieldOptions(Field $field): array
{
if ($field->isStageField()) {
return $this->fetchOpportunityPipelineStages();
}
if ($field->isPipelineField()) {
return $this->fetchOpportunityPipelines();
}
return $this->fetchPropertyOptions('deals', $field->getCrmProviderId());
}
/**
* @throws BadRequest
* @throws HubspotException
*/
public function makeRequest(string $endpoint, $method = 'GET', $payload = [], ?string $queryString = null)
{
$endpoint = self::BASE_URL . $endpoint;
if ($method === 'GET') {
return $this->getInstance()->getClient()?->request(
method: $method,
endpoint: $endpoint,
query_string: $queryString
);
} else {
return $this->getInstance()->getClient()->request($method, $endpoint, [
'json' => ($payload),
]);
}
}
/**
* @throws BadRequest
* @throws HubspotException
*/
public function createMeeting(array $payload): Response
{
$endpoint = '/crm/v3/objects/meetings';
return $this->makeRequest($endpoint, 'POST', $payload);
}
/**
* @throws BadRequest
* @throws HubspotException
*/
public function updateMeeting(string $meetingId, array $payload): Response
{
$endpoint = '/crm/v3/objects/meetings/' . $meetingId;
return $this->makeRequest($endpoint, 'PATCH', $payload);
}
/**
* @throws \Exception
*/
public function createNote(
string $body,
string $ownerId,
int $timestamp,
string $objectId,
NoteObject $noteObject
): ?string {
try {
$noteInput = new SimplePublicObjectInput([
'properties' => [
'hs_note_body' => $body,
'hubspot_owner_id' => $ownerId,
'hs_timestamp' => $timestamp,
],
]);
// Create note
$note = $this->getNewInstance()->crm()->objects()->basicApi()->create('note', $noteInput);
$this->getNewInstance()->crm()->objects()->associationsApi()->create(
'note',
$note->getId(),
$this->getNoteObject($noteObject),
$objectId,
$this->getNoteAssociationType($noteObject),
);
return $note->getId();
} catch (\Exception $e) {
$this->log->error('[Hubspot] Failed to create note', [
'objectId' => $objectId,
'noteObject' => $noteObject->getObjectType(),
'reason' => $e->getMessage(),
]);
\Sentry::captureException($e);
}
return null;
}
public function updateEngagement(string $objectId, array $engagement, array $metadata): void
{
$this->getInstance()->engagements()->update($objectId, $engagement, $metadata);
}
public function getEngagementData(string $engagementId): array
{
$engagement = $this->getInstance()->engagements()->get($engagementId);
return $engagement->toArray();
}
public function createEngagement(array $engagement, array $associations, array $metadata): Response
{
return $this->getInstance()
->engagements()
->create($engagement, $associations, $metadata);
}
public function isUnauthorizedException(\Exception $e): bool
{
// Check for specific HubSpot API exception types first
if ($e instanceof BadRequest) {
// BadRequest can contain 401 status codes
return $e->getCode() === 401;
}
// Check for HTTP client exceptions with status codes
if ($e instanceof \GuzzleHttp\Exception\RequestException && $e->hasResponse()) {
$response = $e->getResponse();
if ($response !== null) {
return $response->getStatusCode() === 401;
}
}
// Check for Guzzle HTTP exceptions
if ($e instanceof \GuzzleHttp\Exception\ClientException) {
return $e->getCode() === 401;
}
// Fallback to string matching as last resort, but be more specific
$message = strtolower($e->getMessage());
return str_contains($message, '401 unauthorized') ||
str_contains($message, 'http 401') ||
str_contains($message, 'status code 401') ||
(preg_match('/\b401\b/', $message) && str_contains($message, 'unauthorized'));
}
/**
* Validates and refreshes the access token if needed before API requests.
* This ensures long-running processes don't fail due to token expiration.
*
* @throws SocialAccountTokenInvalidException
*/
public function ensureValidToken(): void
{
if ($this->oauthAccount === null) {
return;
}
$newToken = $this->tokenManager->ensureValidToken($this->oauthAccount);
if ($newToken !== null) {
$this->accessToken = $newToken;
}
}
public function getConfig()
{
return $this->config;
}
// returns only active (archived=false)
public function getOwners(): array
{
return $this->getNewInstance()->crm()->owners()->getAll();
}
/**
* @param bool $archived
*
* @return array<Owner>|[]
*/
public function getOwnersArchived(bool $archived = true): array
{
$endpoint = '/crm/v3/owners';
$queryParams = [
'archived' => $archived ? 'true' : 'false',
];
$queryString = http_build_query($queryParams);
$owners = [];
try {
$response = $this->makeRequest(endpoint: $endpoint, queryString: $queryString);
$responseData = $response?->toArray();
foreach ($responseData['results'] as $result) {
try {
$owners[] = Owner::create($result);
} catch (Throwable $e) {
$this->log->error('[HubSpot] Failed to process owner data', [
'result' => $result,
'error' => $e->getMessage(),
]);
continue;
}
}
} catch (Throwable $e) {
$this->log->error('HubSpot] Failed to fetch owners', [
'archived' => $archived,
'error' => $e->getMessage(),
]);
return [];
}
return $owners;
}
public function getMeeting(string $engagementId): ObjectWithAssociations
{
return $this->getNewInstance()->crm()->objects()->basicApi()
->getById('meeting', $engagementId, null, 'contact,company,deal');
}
public function deleteEngagement(string $engagementId): void
{
$this->getInstance()->engagements()->delete((int) $engagementId);
}
public function getAssociationsData(array $ids, string $fromObject, string $toObject): array
{
$associationData = [];
$idChunks = array_chunk($ids, self::ASSOCIATIONS_BATCH_SIZE_LIMIT);
foreach ($idChunks as $idChunk) {
try {
$batchInput = new \HubSpot\Client\Crm\Associations\Model\BatchInputPublicObjectId();
$batchInput->setInputs(array_map(function ($id) {
$publicObjectId = new \HubSpot\Client\Crm\Associations\Model\PublicObjectId();
$publicObjectId->setId($id);
return $publicObjectId;
}, $idChunk));
$associatedObjectsData = $this
->getNewInstance()
->crm()
->associations()
->batchApi()
->read($fromObject, $toObject, $batchInput);
if ($associatedObjectsData instanceof \HubSpot\Client\Crm\Associations\Model\BatchResponsePublicAssociationMulti) {
foreach ($associatedObjectsData->getResults() as $association) {
$from = $association->getFrom()->getId();
$toAssociations = $association->getTo();
if (! empty($toAssociations)) {
$associationData[$from] = array_map(function ($item) {
return $item->getId();
}, $toAssociations);
}
}
}
} catch (\Exception $e) {
$this->log->error('[Hubspot] Failed to fetch associations', [
'from_object' => $fromObject,
'to_object' => $toObject,
'reason' => $e->getMessage(),
]);
}
}
return $associationData;
}
/**
* @throws \Exception
*/
private function getNoteAssociationType(NoteObject $noteObject): string
{
return match($noteObject) {
NoteObject::Opportunity => 'note_to_deal',
NoteObject::Lead, NoteObject::Contact => 'note_to_contact', // or 'note_to_lead' if your portal supports it
NoteObject::Account => 'note_to_company',
NoteObject::Call, NoteObject::Event => throw new \Exception('Not supported'),
};
}
/**
* @throws \Exception
*/
private function getNoteObject(NoteObject $noteObject): string
{
return match($noteObject) {
NoteObject::Opportunity => 'deal',
NoteObject::Lead, NoteObject::Contact => 'contact',
NoteObject::Account => 'company',
NoteObject::Call, NoteObject::Event => throw new \Exception('Not supported'),
};
}
public function addAssociations(string $objectType, string $associationType, array $payload): Response
{
$endpoint = "/crm/v4/associations/$objectType/$associationType/batch/create";
return $this->makeRequest($endpoint, 'POST', $payload);
}
public function removeAssociations(string $objectType, string $associationType, array $payload): Response
{
$endpoint = "/crm/v4/associations/$objectType/$associationType/batch/archive";
return $this->makeRequest($endpoint, 'POST', $payload);
}
}
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faVsco.js – Hubspot/Client.php
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2639
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# [PASSWORD_DOTS] HS [PASSWORD_DOTS]
select * from teams where id = 2; # 2
select * from features; # 2
select * from team_features where team_id = 2; # 2
select * from crm_configurations where id = 2; # 2
select * from users where team_id = 2; #
select * from playbooks where team_id = 2; # event 38
select * from playbook_categories where playbook_id = 38; #
SELECT * FROM activities WHERE crm_configuration_id = 2 and crm_provider_id is not null order by id desc;
[URL_WITH_CREDENTIALS] CrmFieldOption array{id:string, label:string, value?:string}
*/
class Client extends BaseClient implements HubspotClientInterface
{
public const string MIN_API_VERSION = '2';
public const string BASE_URL = '[URL_WITH_CREDENTIALS] T
* @param callable(): T $apiCall
* @return T
*
* @throws RateLimitException
*/
private function executeRequest(callable $apiCall)
{
if (! $this->rateLimiter->canMakeRequest($this->config)) {
$retryAfter = $this->rateLimiter->requestAvailableIn($this->config);
$this->log->warning('[Hubspot] Rate limit exceeded, deferring request', [
'team_id' => $this->config->team_id,
'config_id' => $this->config->getId(),
'retry_after' => $retryAfter,
]);
throw new RateLimitException(
'Hubspot rate limit reached for configuration ' . $this->config->getId(),
$retryAfter,
);
}
$this->rateLimiter->incrementRequestCount($this->config);
try {
return $apiCall();
} catch (Throwable $e) {
if ($this->isHubspotRateLimit($e)) {
$retryAfter = $this->parseRetryAfter($e);
$this->log->warning('[Hubspot] Received 429 from API', [
'team_id' => $this->config->team_id,
'config_id' => $this->config->getId(),
'retry_after' => $retryAfter,
'reason' => $e->getMessage(),
]);
throw new RateLimitException('Hubspot returned 429', $retryAfter, $e);
}
throw $e;
}
}
private function isHubspotRateLimit(Throwable $e): bool
{
return method_exists($e, 'getCode') && (int) $e->getCode() === 429;
}
private function parseRetryAfter(Throwable $e): int
{
if (method_exists($e, 'getResponseHeaders')) {
$headers = $e->getResponseHeaders() ?: [];
$value = $headers['Retry-After'] ?? $headers['retry-after'] ?? null;
if (is_array($value)) {
$value = $value[0] ?? null;
}
if (is_numeric($value)) {
return (int) $value;
}
}
return 10;
}
public function getMinimumApiVersion(): string
{
return self::MIN_API_VERSION;
}
public function getInstance(): Factory
{
return new Factory([
'key' => $this->accessToken,
'oauth2' => true,
'base_url' => $this->baseUrl,
]);
}
public function getNewInstance(): Discovery
{
return \HubSpot\Factory::createWithAccessToken($this->accessToken);
}
/**
* Secondly and daily limits for Hubspot API
*
* Product Tier: Free & Starter | Professional & Enterprise | API add-on (any tier)
* Burst: 100/10 seconds | 150/10 seconds | 200/10 seconds
* Daily: 250,000 | 500,000 | 1,000,000
*
* Official documentation states: The search endpoints are rate limited to five requests per second.
* Since with 5 RPS were still hitting secondly rate limits we lowered it to 4
*/
public function getPaginatedData(array $payload, string $type, int $offset = 0): array
{
$total = 0;
$lastId = null;
$rows = [];
foreach ($this->getPaginatedDataGenerator($payload, $type, $offset, $total, $lastId) as $row) {
$rows[] = $row;
}
return ['results' => $rows, 'total' => $total, 'last_record' => $lastId];
}
/**
* @throws HubspotException
* @throws SocialAccountTokenInvalidException
* @throws BadRequest
*/
public function getPaginatedDataGenerator(
array $payload,
string $type,
int $offset = 0,
int &$total = 0,
?string &$lastRecordId = null
): \Generator {
return $this->paginationService->getPaginatedDataGenerator(
$this,
$payload,
$type,
$offset,
$total,
$lastRecordId
);
}
/**
* @throws DealApiException
* @throws CrmException
*/
public function getOpportunityById(string $crmId, array $fields): array
{
try {
$deal = $this->executeRequest(fn () => $this->getNewInstance()->crm()->deals()->basicApi()->getById(
$crmId,
implode(',', $fields),
'companies,contacts'
));
} catch (DealApiException $e) {
$this->log->info('[Hubspot] Failed to fetch opportunity', [
'crm_id' => $crmId,
'reason' => $e->getMessage(),
]);
throw $e;
}
if (! $deal instanceof DealWithAssociations) {
throw new CrmException('Deal not found');
}
return [
'id' => $deal->getId(),
'properties' => $deal->getProperties(),
'associations' => $deal->getAssociations(),
];
}
/**
* Generic batch read method for HubSpot objects
*
* @param string $objectType The object type ('deals', 'companies', 'contacts')
* @param array<string> $crmIds Array of HubSpot object IDs (max 100)
* @param array<string> $fields Array of property names to fetch
*
* @return array<string, array> Array keyed by CRM ID with object data
*/
private function batchReadObjects(string $objectType, array $crmIds, array $fields): array
{
if (empty($crmIds)) {
return [];
}
$this->validateBatchSize($objectType, $crmIds);
$this->ensureValidToken();
try {
$batchConfig = $this->createBatchConfiguration($objectType);
$batchReadRequest = $this->prepareBatchRequest($batchConfig, $crmIds, $fields);
$response = $batchConfig['api']->read($batchReadRequest);
$this->validateApiResponse($response, $objectType);
$results = $this->processApiResults($response);
$this->logBatchResults($objectType, $crmIds, $results);
return $results;
} catch (\Throwable $e) {
$this->handleBatchError($e, $objectType, $crmIds);
}
}
private function validateBatchSize(string $objectType, array $crmIds): void
{
if (count($crmIds) > 100) {
throw new \InvalidArgumentException("Batch size cannot exceed 100 {$objectType}");
}
}
private function createBatchConfiguration(string $objectType): array
{
$configurations = [
'deals' => [
'batchReadRequest' => new \HubSpot\Client\Crm\Deals\Model\BatchReadInputSimplePublicObjectId(),
'inputClass' => \HubSpot\Client\Crm\Deals\Model\SimplePublicObjectId::class,
'api' => $this->getNewInstance()->crm()->deals()->batchApi(),
],
'companies' => [
'batchReadRequest' => new \HubSpot\Client\Crm\Companies\Model\BatchReadInputSimplePublicObjectId(),
'inputClass' => \HubSpot\Client\Crm\Companies\Model\SimplePublicObjectId::class,
'api' => $this->getNewInstance()->crm()->companies()->batchApi(),
],
'contacts' => [
'batchReadRequest' => new \HubSpot\Client\Crm\Contacts\Model\BatchReadInputSimplePublicObjectId(),
'inputClass' => \HubSpot\Client\Crm\Contacts\Model\SimplePublicObjectId::class,
'api' => $this->getNewInstance()->crm()->contacts()->batchApi(),
],
];
if (! isset($configurations[$objectType])) {
throw new \InvalidArgumentException("Unsupported object type: {$objectType}");
}
return $configurations[$objectType];
}
private function prepareBatchRequest(array $batchConfig, array $crmIds, array $fields): object
{
$batchReadRequest = $batchConfig['batchReadRequest'];
$inputClass = $batchConfig['inputClass'];
$inputs = array_map(function ($crmId) use ($inputClass) {
$input = new $inputClass();
$input->setId($crmId);
return $input;
}, $crmIds);
$batchReadRequest->setInputs($inputs);
$batchReadRequest->setProperties($fields);
return $batchReadRequest;
}
private function validateApiResponse($response, string $objectType): void
{
if (! $response) {
throw new CrmException("HubSpot API returned null response for {$objectType} batch read");
}
}
private function processApiResults($response): array
{
$results = [];
$responseResults = $response->getResults();
if ($responseResults) {
foreach ($responseResults as $object) {
if ($object && $object->getId()) {
$results[$object->getId()] = [
'id' => $object->getId(),
'properties' => $object->getProperties() ?: [],
];
}
}
}
return $results;
}
private function logBatchResults(string $objectType, array $crmIds, array $results): void
{
$this->log->info("[HubSpot] Batch fetched {$objectType}", [
'requested_count' => count($crmIds),
'returned_count' => count($results),
'crm_ids' => $crmIds,
]);
}
private function handleBatchError(\Throwable $e, string $objectType, array $crmIds): void
{
$errorMessage = $e->getMessage() ?: 'Unknown error';
$errorTrace = $e->getTraceAsString() ?: 'No trace available';
$this->log->error("[HubSpot] Failed to batch fetch {$objectType}", [
'crm_ids' => $crmIds,
'error' => $errorMessage,
'trace' => $errorTrace,
]);
throw new CrmException("Failed to batch fetch {$objectType}: " . $errorMessage);
}
/**
* Batch read multiple opportunities by their CRM IDs
*
* @param array<string> $crmIds Array of HubSpot deal IDs (max 100)
* @param array<string> $fields Array of property names to fetch
*
* @return array<string, array> Array keyed by CRM ID with opportunity data
*/
public function getOpportunitiesByIds(array $crmIds, array $fields): array
{
return $this->batchReadObjects('deals', $crmIds, $fields);
}
/**
* Batch read multiple companies by their CRM IDs
*
* @param array<string> $crmIds Array of HubSpot company IDs (max 100)
* @param array<string> $fields Array of property names to fetch
*
* @return array<string, array> Array keyed by CRM ID with company data
*/
public function getCompaniesByIds(array $crmIds, array $fields): array
{
return $this->batchReadObjects('companies', $crmIds, $fields);
}
/**
* Batch read multiple contacts by their CRM IDs
*
* @param array<string> $crmIds Array of HubSpot contact IDs (max 100)
* @param array<string> $fields Array of property names to fetch
*
* @return array<string, array> Array keyed by CRM ID with contact data
*/
public function getContactsByIds(array $crmIds, array $fields): array
{
return $this->batchReadObjects('contacts', $crmIds, $fields);
}
/**
* @throws CompanyApiException
* @throws CrmException
*/
public function getAccountById(string $crmId, array $fields): array
{
try {
$company = $this->getNewInstance()->crm()->companies()->basicApi()->getById(
$crmId,
implode(',', $fields),
);
} catch (CompanyApiException $e) {
$this->log->info('[Hubspot] Failed to fetch account', [
'crm_id' => $crmId,
'reason' => $e->getMessage(),
]);
throw $e;
}
if (! $company instanceof CompaniesWithAssociations) {
throw new CrmException('Account not found');
}
return [
'id' => $company->getId(),
'properties' => $company->getProperties(),
];
}
/**
* @throws ContactApiException
* @throws CrmException
*/
public function getContactById(string $crmId, array $fields): array
{
try {
$contact = $this->getNewInstance()->crm()->contacts()->basicApi()->getById(
$crmId,
implode(',', $fields)
);
} catch (ContactApiException $e) {
$this->log->info('[Hubspot] Failed to fetch contact', [
'crm_id' => $crmId,
'reason' => $e->getMessage(),
]);
throw $e;
}
if (! $contact instanceof ContactsWithAssociations) {
throw new CrmException('Contact not found');
}
return [
'id' => $contact->getId(),
'properties' => $contact->getProperties(),
];
}
/**
* This is email search request that Hubspot offers as GET (more generous quota)
*/
public function getContactByEmail(string $email, array $fields = []): array
{
try {
$contact = $this->getNewInstance()->crm()->contacts()->basicApi()->getById(
$email,
implode(',', $fields),
null,
false,
'email'
);
return [
'id' => $contact->getId(),
'properties' => $contact->getProperties(),
];
} catch (ContactApiException $e) {
$this->log->info('[Hubspot] Failed to fetch contact', [
'email' => $email,
'reason' => $e->getMessage(),
]);
return [];
}
}
/**
* @throws CrmException
*/
public function fetchProperty(string $objectType, string $propertyId): Property
{
$result = $this->getNewInstance()->crm()->properties()->coreApi()->getByName($objectType, $propertyId);
if (! $result instanceof Property) {
$this->log->error('[Hubspot] Failed to fetch property', [
'object_type' => $objectType,
'property_id' => $propertyId,
'reason' => $result->getMessage(),
]);
throw new CrmException('Failed to fetch property');
}
return $result;
}
/**
* @return array<CrmFieldOption>
*/
public function fetchPropertyOptions(string $objectType, string $propertyId): array
{
/** @var array<CrmFieldOption> */
return $this->fetchProperty($objectType, $propertyId)->getOptions();
}
/**
* @return array<array{id:string, label:string, deleted:bool}>
*/
public function fetchCallDispositions(): array
{
/** @var Response $response */
$response = $this->getInstance()->engagements()->getCallDispositions();
/**
* @var array<array{
* id:string,
* label:string,
* deleted: bool
* }>
*/
return $response->toArray();
}
/**
* @return array<CrmFieldOption>
*/
public function fetchOpportunityPipelineStages(): array
{
$stages = [];
$apiResponse = $this->getNewInstance()->crm()->pipelines()->pipelinesApi()->getAll('deals');
if ($apiResponse instanceof Error) {
$this->log->error('[Hubspot] Failed to fetch opportunity pipelines', [
'reason' => $apiResponse->getMessage(),
]);
return [];
}
foreach ($apiResponse->getResults() as $pipeline) {
$pipelineStages = array_map(
static function (PipelineStage $stage) {
return [
'id' => $stage->getId(),
'label' => $stage->getLabel(),
];
},
$pipeline->getStages()
);
$stages = array_merge($stages, $pipelineStages);
}
return $stages;
}
public function fetchOpportunityPipelines(): array
{
$pipelines = [];
try {
$apiResponse = $this->makeRequest('/crm/v3/pipelines/deals');
} catch (\Exception $e) {
$this->log->info('[Hubspot] Failed to fetch opportunity pipelines', [
'reason' => $e->getMessage(),
]);
return [];
}
$response = $apiResponse->toArray();
foreach ($response['results'] as $pipeline) {
$pipelines[] = [
'id' => $pipeline['id'],
'label' => $pipeline['label'],
];
}
return $pipelines;
}
/**
* @return array<CrmFieldOption>
*/
public function fetchMeetingOutcomeFieldOptions(Field $field): array
{
return $field->getCrmProviderId() === 'meetingOutcome'
? $this->fetchMeetingOutcomeTypes()
: $this->fetchCallActivityTypes();
}
public function fetchMeetingOutcomeTypes(): array
{
return $this->extractMeetingTypeOptions(
'[URL_WITH_CREDENTIALS] Response $response */
$response = $this->getInstance()
->getClient()
->request('GET', $endpoint);
/**
* @var array<array{
* value: string,
* label: string,
* displayOrder: int
* }> $optionData
*/
$optionData = $response->toArray()['options'] ?? [];
$options = [];
foreach ($optionData as $item) {
$options[] = [
'id' => $item['value'],
'value' => $item['value'],
'label' => $item['label'],
'display_order' => $item['displayOrder'],
];
}
return $options;
}
/**
* @return array<CrmFieldOption>
*/
public function fetchDispositionFieldOptions(): array
{
$options = [];
$dispositions = $this->fetchCallDispositions();
foreach ($dispositions as $disposition) {
if ($disposition['deleted'] !== false) {
continue;
}
$option['value'] = $disposition['id'];
$option['id'] = $disposition['id'];
$option['label'] = $disposition['label'];
$options[] = $option;
}
return $options;
}
/**
* @return array<CrmFieldOption>
*/
public function fetchOpportunityFieldOptions(Field $field): array
{
if ($field->isStageField()) {
return $this->fetchOpportunityPipelineStages();
}
if ($field->isPipelineField()) {
return $this->fetchOpportunityPipelines();
}
return $this->fetchPropertyOptions('deals', $field->getCrmProviderId());
}
/**
* @throws BadRequest
* @throws HubspotException
*/
public function makeRequest(string $endpoint, $method = 'GET', $payload = [], ?string $queryString = null)
{
$endpoint = self::BASE_URL . $endpoint;
if ($method === 'GET') {
$response = $this->getInstance()->getClient()?->request(
method: $method,
endpoint: $endpoint,
query_string: $queryString
);
} else {
return $this->getInstance()->getClient()->request($method, $endpoint, [
'json' => ($payload),
]);
}
}
/**
* @throws BadRequest
* @throws HubspotException
*/
public function createMeeting(array $payload): Response
{
$endpoint = '/crm/v3/objects/meetings';
return $this->makeRequest($endpoint, 'POST', $payload);
}
/**
* @throws BadRequest
* @throws HubspotException
*/
public function updateMeeting(string $meetingId, array $payload): Response
{
$endpoint = '/crm/v3/objects/meetings/' . $meetingId;
return $this->makeRequest($endpoint, 'PATCH', $payload);
}
/**
* @throws \Exception
*/
public function createNote(
string $body,
string $ownerId,
int $timestamp,
string $objectId,
NoteObject $noteObject
): ?string {
try {
$noteInput = new SimplePublicObjectInput([
'properties' => [
'hs_note_body' => $body,
'hubspot_owner_id' => $ownerId,
'hs_timestamp' => $timestamp,
],
]);
// Create note
$note = $this->getNewInstance()->crm()->objects()->basicApi()->create('note', $noteInput);
$this->getNewInstance()->crm()->objects()->associationsApi()->create(
'note',
$note->getId(),
$this->getNoteObject($noteObject),
$objectId,
$this->getNoteAssociationType($noteObject),
);
return $note->getId();
} catch (\Exception $e) {
$this->log->error('[Hubspot] Failed to create note', [
'objectId' => $objectId,
'noteObject' => $noteObject->getObjectType(),
'reason' => $e->getMessage(),
]);
\Sentry::captureException($e);
}
return null;
}
public function updateEngagement(string $objectId, array $engagement, array $metadata): void
{
$this->getInstance()->engagements()->update($objectId, $engagement, $metadata);
}
public function getEngagementData(string $engagementId): array
{
$engagement = $this->getInstance()->engagements()->get($engagementId);
return $engagement->toArray();
}
public function createEngagement(array $engagement, array $associations, array $metadata): Response
{
return $this->getInstance()
->engagements()
->create($engagement, $associations, $metadata);
}
public function isUnauthorizedException(\Exception $e): bool
{
// Check for specific HubSpot API exception types first
if ($e instanceof BadRequest) {
// BadRequest can contain 401 status codes
return $e->getCode() === 401;
}
// Check for HTTP client exceptions with status codes
if ($e instanceof \GuzzleHttp\Exception\RequestException && $e->hasResponse()) {
$response = $e->getResponse();
if ($response !== null) {
return $response->getStatusCode() === 401;
}
}
// Check for Guzzle HTTP exceptions
if ($e instanceof \GuzzleHttp\Exception\ClientException) {
return $e->getCode() === 401;
}
// Fallback to string matching as last resort, but be more specific
$message = strtolower($e->getMessage());
return str_contains($message, '401 unauthorized') ||
str_contains($message, 'http 401') ||
str_contains($message, 'status code 401') ||
(preg_match('/\b401\b/', $message) && str_contains($message, 'unauthorized'));
}
/**
* Validates and refreshes the access token if needed before API requests.
* This ensures long-running processes don't fail due to token expiration.
*
* @throws SocialAccountTokenInvalidException
*/
public function ensureValidToken(): void
{
if ($this->oauthAccount === null) {
return;
}
$newToken = $this->tokenManager->ensureValidToken($this->oauthAccount);
if ($newToken !== null) {
$this->accessToken = $newToken;
}
}
public function getConfig()
{
return $this->config;
}
// returns only active (archived=false)
public function getOwners(): array
{
return $this->getNewInstance()->crm()->owners()->getAll();
}
/**
* @param bool $archived
*
* @return array<Owner>|[]
*/
public function getOwnersArchived(bool $archived = true): array
{
$endpoint = '/crm/v3/owners';
$queryParams = [
'archived' => $archived ? 'true' : 'false',
];
$queryString = http_build_query($queryParams);
$owners = [];
try {
$response = $this->makeRequest(endpoint: $endpoint, queryString: $queryString);
$responseData = $response?->toArray();
foreach ($responseData['results'] as $result) {
try {
$owners[] = Owner::create($result);
} catch (Throwable $e) {
$this->log->error('[HubSpot] Failed to process owner data', [
'result' => $result,
'error' => $e->getMessage(),
]);
continue;
}
}
} catch (Throwable $e) {
$this->log->error('HubSpot] Failed to fetch owners', [
'archived' => $archived,
'error' => $e->getMessage(),
]);
return [];
}
return $owners;
}
public function getMeeting(string $engagementId): ObjectWithAssociations
{
return $this->getNewInstance()->crm()->objects()->basicApi()
->getById('meeting', $engagementId, null, 'contact,company,deal');
}
public function deleteEngagement(string $engagementId): void
{
$this->getInstance()->engagements()->delete((int) $engagementId);
}
public function getAssociationsData(array $ids, string $fromObject, string $toObject): array
{
$associationData = [];
$idChunks = array_chunk($ids, self::ASSOCIATIONS_BATCH_SIZE_LIMIT);
foreach ($idChunks as $idChunk) {
try {
$batchInput = new \HubSpot\Client\Crm\Associations\Model\BatchInputPublicObjectId();
$batchInput->setInputs(array_map(function ($id) {
$publicObjectId = new \HubSpot\Client\Crm\Associations\Model\PublicObjectId();
$publicObjectId->setId($id);
return $publicObjectId;
}, $idChunk));
$associatedObjectsData = $this
->getNewInstance()
->crm()
->associations()
->batchApi()
->read($fromObject, $toObject, $batchInput);
if ($associatedObjectsData instanceof \HubSpot\Client\Crm\Associations\Model\BatchResponsePublicAssociationMulti) {
foreach ($associatedObjectsData->getResults() as $association) {
$from = $association->getFrom()->getId();
$toAssociations = $association->getTo();
if (! empty($toAssociations)) {
$associationData[$from] = array_map(function ($item) {
return $item->getId();
}, $toAssociations);
}
}
}
} catch (\Exception $e) {
$this->log->error('[Hubspot] Failed to fetch associations', [
'from_object' => $fromObject,
'to_object' => $toObject,
'reason' => $e->getMessage(),
]);
}
}
return $associationData;
}
/**
* @throws \Exception
*/
private function getNoteAssociationType(NoteObject $noteObject): string
{
return match($noteObject) {
NoteObject::Opportunity => 'note_to_deal',
NoteObject::Lead, NoteObject::Contact => 'note_to_contact', // or 'note_to_lead' if your portal supports it
NoteObject::Account => 'note_to_company',
NoteObject::Call, NoteObject::Event => throw new \Exception('Not supported'),
};
}
/**
* @throws \Exception
*/
private function getNoteObject(NoteObject $noteObject): string
{
return match($noteObject) {
NoteObject::Opportunity => 'deal',
NoteObject::Lead, NoteObject::Contact => 'contact',
NoteObject::Account => 'company',
NoteObject::Call, NoteObject::Event => throw new \Exception('Not supported'),
};
}
public function addAssociations(string $objectType, string $associationType, array $payload): Response
{
$endpoint = "/crm/v4/associations/$objectType/$associationType/batch/create";
return $this->makeRequest($endpoint, 'POST', $payload);
}
public function removeAssociations(string $objectType, string $associationType, array $payload): Response
{
$endpoint = "/crm/v4/associations/$objectType/$associationType/batch/archive";
return $this->makeRequest($endpoint, 'POST', $payload);
}
}
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AskJiminnyReportActivityServiceTest
Run 'AskJiminnyReportActivityServiceTest'
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# [PASSWORD_DOTS] HS [PASSWORD_DOTS]
select * from teams where id = 2; # 2
select * from features; # 2
select * from team_features where team_id = 2; # 2
select * from crm_configurations where id = 2; # 2
select * from users where team_id = 2; #
select * from playbooks where team_id = 2; # event 38
select * from playbook_categories where playbook_id = 38; #
SELECT * FROM activities WHERE crm_configuration_id = 2 and crm_provider_id is not null order by id desc;
[URL_WITH_CREDENTIALS] CrmFieldOption array{id:string, label:string, value?:string}
*/
class Client extends BaseClient implements HubspotClientInterface
{
public const string MIN_API_VERSION = '2';
public const string BASE_URL = '[URL_WITH_CREDENTIALS] T
* @param callable(): T $apiCall
* @return T
*
* @throws RateLimitException
*/
private function executeRequest(callable $apiCall)
{
if (! $this->rateLimiter->canMakeRequest($this->config)) {
$retryAfter = $this->rateLimiter->requestAvailableIn($this->config);
$this->log->warning('[Hubspot] Rate limit exceeded, deferring request', [
'team_id' => $this->config->team_id,
'config_id' => $this->config->getId(),
'retry_after' => $retryAfter,
]);
throw new RateLimitException(
'Hubspot rate limit reached for configuration ' . $this->config->getId(),
$retryAfter,
);
}
$this->rateLimiter->incrementRequestCount($this->config);
try {
return $apiCall();
} catch (Throwable $e) {
if ($this->isHubspotRateLimit($e)) {
$retryAfter = $this->parseRetryAfter($e);
$this->log->warning('[Hubspot] Received 429 from API', [
'team_id' => $this->config->team_id,
'config_id' => $this->config->getId(),
'retry_after' => $retryAfter,
'reason' => $e->getMessage(),
]);
throw new RateLimitException('Hubspot returned 429', $retryAfter, $e);
}
throw $e;
}
}
private function isHubspotRateLimit(Throwable $e): bool
{
return method_exists($e, 'getCode') && (int) $e->getCode() === 429;
}
private function parseRetryAfter(Throwable $e): int
{
if (method_exists($e, 'getResponseHeaders')) {
$headers = $e->getResponseHeaders() ?: [];
$value = $headers['Retry-After'] ?? $headers['retry-after'] ?? null;
if (is_array($value)) {
$value = $value[0] ?? null;
}
if (is_numeric($value)) {
return (int) $value;
}
}
return 10;
}
public function getMinimumApiVersion(): string
{
return self::MIN_API_VERSION;
}
public function getInstance(): Factory
{
return new Factory([
'key' => $this->accessToken,
'oauth2' => true,
'base_url' => $this->baseUrl,
]);
}
public function getNewInstance(): Discovery
{
return \HubSpot\Factory::createWithAccessToken($this->accessToken);
}
/**
* Secondly and daily limits for Hubspot API
*
* Product Tier: Free & Starter | Professional & Enterprise | API add-on (any tier)
* Burst: 100/10 seconds | 150/10 seconds | 200/10 seconds
* Daily: 250,000 | 500,000 | 1,000,000
*
* Official documentation states: The search endpoints are rate limited to five requests per second.
* Since with 5 RPS were still hitting secondly rate limits we lowered it to 4
*/
public function getPaginatedData(array $payload, string $type, int $offset = 0): array
{
$total = 0;
$lastId = null;
$rows = [];
foreach ($this->getPaginatedDataGenerator($payload, $type, $offset, $total, $lastId) as $row) {
$rows[] = $row;
}
return ['results' => $rows, 'total' => $total, 'last_record' => $lastId];
}
/**
* @throws HubspotException
* @throws SocialAccountTokenInvalidException
* @throws BadRequest
*/
public function getPaginatedDataGenerator(
array $payload,
string $type,
int $offset = 0,
int &$total = 0,
?string &$lastRecordId = null
): \Generator {
return $this->paginationService->getPaginatedDataGenerator(
$this,
$payload,
$type,
$offset,
$total,
$lastRecordId
);
}
/**
* @throws DealApiException
* @throws CrmException
*/
public function getOpportunityById(string $crmId, array $fields): array
{
try {
$deal = $this->executeRequest(fn () => $this->getNewInstance()->crm()->deals()->basicApi()->getById(
$crmId,
implode(',', $fields),
'companies,contacts'
));
} catch (DealApiException $e) {
$this->log->info('[Hubspot] Failed to fetch opportunity', [
'crm_id' => $crmId,
'reason' => $e->getMessage(),
]);
throw $e;
}
if (! $deal instanceof DealWithAssociations) {
throw new CrmException('Deal not found');
}
return [
'id' => $deal->getId(),
'properties' => $deal->getProperties(),
'associations' => $deal->getAssociations(),
];
}
/**
* Generic batch read method for HubSpot objects
*
* @param string $objectType The object type ('deals', 'companies', 'contacts')
* @param array<string> $crmIds Array of HubSpot object IDs (max 100)
* @param array<string> $fields Array of property names to fetch
*
* @return array<string, array> Array keyed by CRM ID with object data
*/
private function batchReadObjects(string $objectType, array $crmIds, array $fields): array
{
if (empty($crmIds)) {
return [];
}
$this->validateBatchSize($objectType, $crmIds);
$this->ensureValidToken();
try {
$batchConfig = $this->createBatchConfiguration($objectType);
$batchReadRequest = $this->prepareBatchRequest($batchConfig, $crmIds, $fields);
$response = $batchConfig['api']->read($batchReadRequest);
$this->validateApiResponse($response, $objectType);
$results = $this->processApiResults($response);
$this->logBatchResults($objectType, $crmIds, $results);
return $results;
} catch (\Throwable $e) {
$this->handleBatchError($e, $objectType, $crmIds);
}
}
private function validateBatchSize(string $objectType, array $crmIds): void
{
if (count($crmIds) > 100) {
throw new \InvalidArgumentException("Batch size cannot exceed 100 {$objectType}");
}
}
private function createBatchConfiguration(string $objectType): array
{
$configurations = [
'deals' => [
'batchReadRequest' => new \HubSpot\Client\Crm\Deals\Model\BatchReadInputSimplePublicObjectId(),
'inputClass' => \HubSpot\Client\Crm\Deals\Model\SimplePublicObjectId::class,
'api' => $this->getNewInstance()->crm()->deals()->batchApi(),
],
'companies' => [
'batchReadRequest' => new \HubSpot\Client\Crm\Companies\Model\BatchReadInputSimplePublicObjectId(),
'inputClass' => \HubSpot\Client\Crm\Companies\Model\SimplePublicObjectId::class,
'api' => $this->getNewInstance()->crm()->companies()->batchApi(),
],
'contacts' => [
'batchReadRequest' => new \HubSpot\Client\Crm\Contacts\Model\BatchReadInputSimplePublicObjectId(),
'inputClass' => \HubSpot\Client\Crm\Contacts\Model\SimplePublicObjectId::class,
'api' => $this->getNewInstance()->crm()->contacts()->batchApi(),
],
];
if (! isset($configurations[$objectType])) {
throw new \InvalidArgumentException("Unsupported object type: {$objectType}");
}
return $configurations[$objectType];
}
private function prepareBatchRequest(array $batchConfig, array $crmIds, array $fields): object
{
$batchReadRequest = $batchConfig['batchReadRequest'];
$inputClass = $batchConfig['inputClass'];
$inputs = array_map(function ($crmId) use ($inputClass) {
$input = new $inputClass();
$input->setId($crmId);
return $input;
}, $crmIds);
$batchReadRequest->setInputs($inputs);
$batchReadRequest->setProperties($fields);
return $batchReadRequest;
}
private function validateApiResponse($response, string $objectType): void
{
if (! $response) {
throw new CrmException("HubSpot API returned null response for {$objectType} batch read");
}
}
private function processApiResults($response): array
{
$results = [];
$responseResults = $response->getResults();
if ($responseResults) {
foreach ($responseResults as $object) {
if ($object && $object->getId()) {
$results[$object->getId()] = [
'id' => $object->getId(),
'properties' => $object->getProperties() ?: [],
];
}
}
}
return $results;
}
private function logBatchResults(string $objectType, array $crmIds, array $results): void
{
$this->log->info("[HubSpot] Batch fetched {$objectType}", [
'requested_count' => count($crmIds),
'returned_count' => count($results),
'crm_ids' => $crmIds,
]);
}
private function handleBatchError(\Throwable $e, string $objectType, array $crmIds): void
{
$errorMessage = $e->getMessage() ?: 'Unknown error';
$errorTrace = $e->getTraceAsString() ?: 'No trace available';
$this->log->error("[HubSpot] Failed to batch fetch {$objectType}", [
'crm_ids' => $crmIds,
'error' => $errorMessage,
'trace' => $errorTrace,
]);
throw new CrmException("Failed to batch fetch {$objectType}: " . $errorMessage);
}
/**
* Batch read multiple opportunities by their CRM IDs
*
* @param array<string> $crmIds Array of HubSpot deal IDs (max 100)
* @param array<string> $fields Array of property names to fetch
*
* @return array<string, array> Array keyed by CRM ID with opportunity data
*/
public function getOpportunitiesByIds(array $crmIds, array $fields): array
{
return $this->batchReadObjects('deals', $crmIds, $fields);
}
/**
* Batch read multiple companies by their CRM IDs
*
* @param array<string> $crmIds Array of HubSpot company IDs (max 100)
* @param array<string> $fields Array of property names to fetch
*
* @return array<string, array> Array keyed by CRM ID with company data
*/
public function getCompaniesByIds(array $crmIds, array $fields): array
{
return $this->batchReadObjects('companies', $crmIds, $fields);
}
/**
* Batch read multiple contacts by their CRM IDs
*
* @param array<string> $crmIds Array of HubSpot contact IDs (max 100)
* @param array<string> $fields Array of property names to fetch
*
* @return array<string, array> Array keyed by CRM ID with contact data
*/
public function getContactsByIds(array $crmIds, array $fields): array
{
return $this->batchReadObjects('contacts', $crmIds, $fields);
}
/**
* @throws CompanyApiException
* @throws CrmException
*/
public function getAccountById(string $crmId, array $fields): array
{
try {
$company = $this->getNewInstance()->crm()->companies()->basicApi()->getById(
$crmId,
implode(',', $fields),
);
} catch (CompanyApiException $e) {
$this->log->info('[Hubspot] Failed to fetch account', [
'crm_id' => $crmId,
'reason' => $e->getMessage(),
]);
throw $e;
}
if (! $company instanceof CompaniesWithAssociations) {
throw new CrmException('Account not found');
}
return [
'id' => $company->getId(),
'properties' => $company->getProperties(),
];
}
/**
* @throws ContactApiException
* @throws CrmException
*/
public function getContactById(string $crmId, array $fields): array
{
try {
$contact = $this->getNewInstance()->crm()->contacts()->basicApi()->getById(
$crmId,
implode(',', $fields)
);
} catch (ContactApiException $e) {
$this->log->info('[Hubspot] Failed to fetch contact', [
'crm_id' => $crmId,
'reason' => $e->getMessage(),
]);
throw $e;
}
if (! $contact instanceof ContactsWithAssociations) {
throw new CrmException('Contact not found');
}
return [
'id' => $contact->getId(),
'properties' => $contact->getProperties(),
];
}
/**
* This is email search request that Hubspot offers as GET (more generous quota)
*/
public function getContactByEmail(string $email, array $fields = []): array
{
try {
$contact = $this->getNewInstance()->crm()->contacts()->basicApi()->getById(
$email,
implode(',', $fields),
null,
false,
'email'
);
return [
'id' => $contact->getId(),
'properties' => $contact->getProperties(),
];
} catch (ContactApiException $e) {
$this->log->info('[Hubspot] Failed to fetch contact', [
'email' => $email,
'reason' => $e->getMessage(),
]);
return [];
}
}
/**
* @throws CrmException
*/
public function fetchProperty(string $objectType, string $propertyId): Property
{
$result = $this->getNewInstance()->crm()->properties()->coreApi()->getByName($objectType, $propertyId);
if (! $result instanceof Property) {
$this->log->error('[Hubspot] Failed to fetch property', [
'object_type' => $objectType,
'property_id' => $propertyId,
'reason' => $result->getMessage(),
]);
throw new CrmException('Failed to fetch property');
}
return $result;
}
/**
* @return array<CrmFieldOption>
*/
public function fetchPropertyOptions(string $objectType, string $propertyId): array
{
/** @var array<CrmFieldOption> */
return $this->fetchProperty($objectType, $propertyId)->getOptions();
}
/**
* @return array<array{id:string, label:string, deleted:bool}>
*/
public function fetchCallDispositions(): array
{
/** @var Response $response */
$response = $this->getInstance()->engagements()->getCallDispositions();
/**
* @var array<array{
* id:string,
* label:string,
* deleted: bool
* }>
*/
return $response->toArray();
}
/**
* @return array<CrmFieldOption>
*/
public function fetchOpportunityPipelineStages(): array
{
$stages = [];
$apiResponse = $this->getNewInstance()->crm()->pipelines()->pipelinesApi()->getAll('deals');
if ($apiResponse instanceof Error) {
$this->log->error('[Hubspot] Failed to fetch opportunity pipelines', [
'reason' => $apiResponse->getMessage(),
]);
return [];
}
foreach ($apiResponse->getResults() as $pipeline) {
$pipelineStages = array_map(
static function (PipelineStage $stage) {
return [
'id' => $stage->getId(),
'label' => $stage->getLabel(),
];
},
$pipeline->getStages()
);
$stages = array_merge($stages, $pipelineStages);
}
return $stages;
}
public function fetchOpportunityPipelines(): array
{
$pipelines = [];
try {
$apiResponse = $this->makeRequest('/crm/v3/pipelines/deals');
} catch (\Exception $e) {
$this->log->info('[Hubspot] Failed to fetch opportunity pipelines', [
'reason' => $e->getMessage(),
]);
return [];
}
$response = $apiResponse->toArray();
foreach ($response['results'] as $pipeline) {
$pipelines[] = [
'id' => $pipeline['id'],
'label' => $pipeline['label'],
];
}
return $pipelines;
}
/**
* @return array<CrmFieldOption>
*/
public function fetchMeetingOutcomeFieldOptions(Field $field): array
{
return $field->getCrmProviderId() === 'meetingOutcome'
? $this->fetchMeetingOutcomeTypes()
: $this->fetchCallActivityTypes();
}
public function fetchMeetingOutcomeTypes(): array
{
return $this->extractMeetingTypeOptions(
'[URL_WITH_CREDENTIALS] Response $response */
$response = $this->getInstance()
->getClient()
->request('GET', $endpoint);
/**
* @var array<array{
* value: string,
* label: string,
* displayOrder: int
* }> $optionData
*/
$optionData = $response->toArray()['options'] ?? [];
$options = [];
foreach ($optionData as $item) {
$options[] = [
'id' => $item['value'],
'value' => $item['value'],
'label' => $item['label'],
'display_order' => $item['displayOrder'],
];
}
return $options;
}
/**
* @return array<CrmFieldOption>
*/
public function fetchDispositionFieldOptions(): array
{
$options = [];
$dispositions = $this->fetchCallDispositions();
foreach ($dispositions as $disposition) {
if ($disposition['deleted'] !== false) {
continue;
}
$option['value'] = $disposition['id'];
$option['id'] = $disposition['id'];
$option['label'] = $disposition['label'];
$options[] = $option;
}
return $options;
}
/**
* @return array<CrmFieldOption>
*/
public function fetchOpportunityFieldOptions(Field $field): array
{
if ($field->isStageField()) {
return $this->fetchOpportunityPipelineStages();
}
if ($field->isPipelineField()) {
return $this->fetchOpportunityPipelines();
}
return $this->fetchPropertyOptions('deals', $field->getCrmProviderId());
}
/**
* @throws BadRequest
* @throws HubspotException
*/
public function makeRequest(string $endpoint, $method = 'GET', $payload = [], ?string $queryString = null)
{
$endpoint = self::BASE_URL . $endpoint;
if ($method === 'GET') {
$response = $this->getInstance()->getClient()?->request(
method: $method,
endpoint: $endpoint,
query_string: $queryString
);
} else {
return $this->getInstance()->getClient()->request($method, $endpoint, [
'json' => ($payload),
]);
}
}
/**
* @throws BadRequest
* @throws HubspotException
*/
public function createMeeting(array $payload): Response
{
$endpoint = '/crm/v3/objects/meetings';
return $this->makeRequest($endpoint, 'POST', $payload);
}
/**
* @throws BadRequest
* @throws HubspotException
*/
public function updateMeeting(string $meetingId, array $payload): Response
{
$endpoint = '/crm/v3/objects/meetings/' . $meetingId;
return $this->makeRequest($endpoint, 'PATCH', $payload);
}
/**
* @throws \Exception
*/
public function createNote(
string $body,
string $ownerId,
int $timestamp,
string $objectId,
NoteObject $noteObject
): ?string {
try {
$noteInput = new SimplePublicObjectInput([
'properties' => [
'hs_note_body' => $body,
'hubspot_owner_id' => $ownerId,
'hs_timestamp' => $timestamp,
],
]);
// Create note
$note = $this->getNewInstance()->crm()->objects()->basicApi()->create('note', $noteInput);
$this->getNewInstance()->crm()->objects()->associationsApi()->create(
'note',
$note->getId(),
$this->getNoteObject($noteObject),
$objectId,
$this->getNoteAssociationType($noteObject),
);
return $note->getId();
} catch (\Exception $e) {
$this->log->error('[Hubspot] Failed to create note', [
'objectId' => $objectId,
'noteObject' => $noteObject->getObjectType(),
'reason' => $e->getMessage(),
]);
\Sentry::captureException($e);
}
return null;
}
public function updateEngagement(string $objectId, array $engagement, array $metadata): void
{
$this->getInstance()->engagements()->update($objectId, $engagement, $metadata);
}
public function getEngagementData(string $engagementId): array
{
$engagement = $this->getInstance()->engagements()->get($engagementId);
return $engagement->toArray();
}
public function createEngagement(array $engagement, array $associations, array $metadata): Response
{
return $this->getInstance()
->engagements()
->create($engagement, $associations, $metadata);
}
public function isUnauthorizedException(\Exception $e): bool
{
// Check for specific HubSpot API exception types first
if ($e instanceof BadRequest) {
// BadRequest can contain 401 status codes
return $e->getCode() === 401;
}
// Check for HTTP client exceptions with status codes
if ($e instanceof \GuzzleHttp\Exception\RequestException && $e->hasResponse()) {
$response = $e->getResponse();
if ($response !== null) {
return $response->getStatusCode() === 401;
}
}
// Check for Guzzle HTTP exceptions
if ($e instanceof \GuzzleHttp\Exception\ClientException) {
return $e->getCode() === 401;
}
// Fallback to string matching as last resort, but be more specific
$message = strtolower($e->getMessage());
return str_contains($message, '401 unauthorized') ||
str_contains($message, 'http 401') ||
str_contains($message, 'status code 401') ||
(preg_match('/\b401\b/', $message) && str_contains($message, 'unauthorized'));
}
/**
* Validates and refreshes the access token if needed before API requests.
* This ensures long-running processes don't fail due to token expiration.
*
* @throws SocialAccountTokenInvalidException
*/
public function ensureValidToken(): void
{
if ($this->oauthAccount === null) {
return;
}
$newToken = $this->tokenManager->ensureValidToken($this->oauthAccount);
if ($newToken !== null) {
$this->accessToken = $newToken;
}
}
public function getConfig()
{
return $this->config;
}
// returns only active (archived=false)
public function getOwners(): array
{
return $this->getNewInstance()->crm()->owners()->getAll();
}
/**
* @param bool $archived
*
* @return array<Owner>|[]
*/
public function getOwnersArchived(bool $archived = true): array
{
$endpoint = '/crm/v3/owners';
$queryParams = [
'archived' => $archived ? 'true' : 'false',
];
$queryString = http_build_query($queryParams);
$owners = [];
try {
$response = $this->makeRequest(endpoint: $endpoint, queryString: $queryString);
$responseData = $response?->toArray();
foreach ($responseData['results'] as $result) {
try {
$owners[] = Owner::create($result);
} catch (Throwable $e) {
$this->log->error('[HubSpot] Failed to process owner data', [
'result' => $result,
'error' => $e->getMessage(),
]);
continue;
}
}
} catch (Throwable $e) {
$this->log->error('HubSpot] Failed to fetch owners', [
'archived' => $archived,
'error' => $e->getMessage(),
]);
return [];
}
return $owners;
}
public function getMeeting(string $engagementId): ObjectWithAssociations
{
return $this->getNewInstance()->crm()->objects()->basicApi()
->getById('meeting', $engagementId, null, 'contact,company,deal');
}
public function deleteEngagement(string $engagementId): void
{
$this->getInstance()->engagements()->delete((int) $engagementId);
}
public function getAssociationsData(array $ids, string $fromObject, string $toObject): array
{
$associationData = [];
$idChunks = array_chunk($ids, self::ASSOCIATIONS_BATCH_SIZE_LIMIT);
foreach ($idChunks as $idChunk) {
try {
$batchInput = new \HubSpot\Client\Crm\Associations\Model\BatchInputPublicObjectId();
$batchInput->setInputs(array_map(function ($id) {
$publicObjectId = new \HubSpot\Client\Crm\Associations\Model\PublicObjectId();
$publicObjectId->setId($id);
return $publicObjectId;
}, $idChunk));
$associatedObjectsData = $this
->getNewInstance()
->crm()
->associations()
->batchApi()
->read($fromObject, $toObject, $batchInput);
if ($associatedObjectsData instanceof \HubSpot\Client\Crm\Associations\Model\BatchResponsePublicAssociationMulti) {
foreach ($associatedObjectsData->getResults() as $association) {
$from = $association->getFrom()->getId();
$toAssociations = $association->getTo();
if (! empty($toAssociations)) {
$associationData[$from] = array_map(function ($item) {
return $item->getId();
}, $toAssociations);
}
}
}
} catch (\Exception $e) {
$this->log->error('[Hubspot] Failed to fetch associations', [
'from_object' => $fromObject,
'to_object' => $toObject,
'reason' => $e->getMessage(),
]);
}
}
return $associationData;
}
/**
* @throws \Exception
*/
private function getNoteAssociationType(NoteObject $noteObject): string
{
return match($noteObject) {
NoteObject::Opportunity => 'note_to_deal',
NoteObject::Lead, NoteObject::Contact => 'note_to_contact', // or 'note_to_lead' if your portal supports it
NoteObject::Account => 'note_to_company',
NoteObject::Call, NoteObject::Event => throw new \Exception('Not supported'),
};
}
/**
* @throws \Exception
*/
private function getNoteObject(NoteObject $noteObject): string
{
return match($noteObject) {
NoteObject::Opportunity => 'deal',
NoteObject::Lead, NoteObject::Contact => 'contact',
NoteObject::Account => 'company',
NoteObject::Call, NoteObject::Event => throw new \Exception('Not supported'),
};
}
public function addAssociations(string $objectType, string $associationType, array $payload): Response
{
$endpoint = "/crm/v4/associations/$objectType/$associationType/batch/create";
return $this->makeRequest($endpoint, 'POST', $payload);
}
public function removeAssociations(string $objectType, string $associationType, array $payload): Response
{
$endpoint = "/crm/v4/associations/$objectType/$associationType/batch/archive";
return $this->makeRequest($endpoint, 'POST', $payload);
}
}
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# [PASSWORD_DOTS] HS [PASSWORD_DOTS]
select * from teams where id = 2; # 2
select * from features; # 2
select * from team_features where team_id = 2; # 2
select * from crm_configurations where id = 2; # 2
select * from users where team_id = 2; #
select * from playbooks where team_id = 2; # event 38
select * from playbook_categories where playbook_id = 38; #
SELECT * FROM activities WHERE crm_configuration_id = 2 and crm_provider_id is not null order by id desc;
[URL_WITH_CREDENTIALS] CrmFieldOption array{id:string, label:string, value?:string}
*/
class Client extends BaseClient implements HubspotClientInterface
{
public const string MIN_API_VERSION = '2';
public const string BASE_URL = '[URL_WITH_CREDENTIALS] T
* @param callable(): T $apiCall
* @return T
*
* @throws RateLimitException
*/
private function executeRequest(callable $apiCall)
{
if (! $this->rateLimiter->canMakeRequest($this->config)) {
$retryAfter = $this->rateLimiter->requestAvailableIn($this->config);
$this->log->warning('[Hubspot] Rate limit exceeded, deferring request', [
'team_id' => $this->config->team_id,
'config_id' => $this->config->getId(),
'retry_after' => $retryAfter,
]);
throw new RateLimitException(
'Hubspot rate limit reached for configuration ' . $this->config->getId(),
$retryAfter,
);
}
$this->rateLimiter->incrementRequestCount($this->config);
try {
return $apiCall();
} catch (Throwable $e) {
if ($this->isHubspotRateLimit($e)) {
$retryAfter = $this->parseRetryAfter($e);
$this->log->warning('[Hubspot] Received 429 from API', [
'team_id' => $this->config->team_id,
'config_id' => $this->config->getId(),
'retry_after' => $retryAfter,
'reason' => $e->getMessage(),
]);
throw new RateLimitException('Hubspot returned 429', $retryAfter, $e);
}
throw $e;
}
}
private function isHubspotRateLimit(Throwable $e): bool
{
return method_exists($e, 'getCode') && (int) $e->getCode() === 429;
}
private function parseRetryAfter(Throwable $e): int
{
if (method_exists($e, 'getResponseHeaders')) {
$headers = $e->getResponseHeaders() ?: [];
$value = $headers['Retry-After'] ?? $headers['retry-after'] ?? null;
if (is_array($value)) {
$value = $value[0] ?? null;
}
if (is_numeric($value)) {
return (int) $value;
}
}
return 10;
}
public function getMinimumApiVersion(): string
{
return self::MIN_API_VERSION;
}
public function getInstance(): Factory
{
return new Factory([
'key' => $this->accessToken,
'oauth2' => true,
'base_url' => $this->baseUrl,
]);
}
public function getNewInstance(): Discovery
{
return \HubSpot\Factory::createWithAccessToken($this->accessToken);
}
/**
* Secondly and daily limits for Hubspot API
*
* Product Tier: Free & Starter | Professional & Enterprise | API add-on (any tier)
* Burst: 100/10 seconds | 150/10 seconds | 200/10 seconds
* Daily: 250,000 | 500,000 | 1,000,000
*
* Official documentation states: The search endpoints are rate limited to five requests per second.
* Since with 5 RPS were still hitting secondly rate limits we lowered it to 4
*/
public function getPaginatedData(array $payload, string $type, int $offset = 0): array
{
$total = 0;
$lastId = null;
$rows = [];
foreach ($this->getPaginatedDataGenerator($payload, $type, $offset, $total, $lastId) as $row) {
$rows[] = $row;
}
return ['results' => $rows, 'total' => $total, 'last_record' => $lastId];
}
/**
* @throws HubspotException
* @throws SocialAccountTokenInvalidException
* @throws BadRequest
*/
public function getPaginatedDataGenerator(
array $payload,
string $type,
int $offset = 0,
int &$total = 0,
?string &$lastRecordId = null
): \Generator {
return $this->paginationService->getPaginatedDataGenerator(
$this,
$payload,
$type,
$offset,
$total,
$lastRecordId
);
}
/**
* @throws DealApiException
* @throws CrmException
*/
public function getOpportunityById(string $crmId, array $fields): array
{
try {
$deal = $this->executeRequest(fn () => $this->getNewInstance()->crm()->deals()->basicApi()->getById(
$crmId,
implode(',', $fields),
'companies,contacts'
));
} catch (DealApiException $e) {
$this->log->info('[Hubspot] Failed to fetch opportunity', [
'crm_id' => $crmId,
'reason' => $e->getMessage(),
]);
throw $e;
}
if (! $deal instanceof DealWithAssociations) {
throw new CrmException('Deal not found');
}
return [
'id' => $deal->getId(),
'properties' => $deal->getProperties(),
'associations' => $deal->getAssociations(),
];
}
/**
* Generic batch read method for HubSpot objects
*
* @param string $objectType The object type ('deals', 'companies', 'contacts')
* @param array<string> $crmIds Array of HubSpot object IDs (max 100)
* @param array<string> $fields Array of property names to fetch
*
* @return array<string, array> Array keyed by CRM ID with object data
*/
private function batchReadObjects(string $objectType, array $crmIds, array $fields): array
{
if (empty($crmIds)) {
return [];
}
$this->validateBatchSize($objectType, $crmIds);
$this->ensureValidToken();
try {
$batchConfig = $this->createBatchConfiguration($objectType);
$batchReadRequest = $this->prepareBatchRequest($batchConfig, $crmIds, $fields);
$response = $batchConfig['api']->read($batchReadRequest);
$this->validateApiResponse($response, $objectType);
$results = $this->processApiResults($response);
$this->logBatchResults($objectType, $crmIds, $results);
return $results;
} catch (\Throwable $e) {
$this->handleBatchError($e, $objectType, $crmIds);
}
}
private function validateBatchSize(string $objectType, array $crmIds): void
{
if (count($crmIds) > 100) {
throw new \InvalidArgumentException("Batch size cannot exceed 100 {$objectType}");
}
}
private function createBatchConfiguration(string $objectType): array
{
$configurations = [
'deals' => [
'batchReadRequest' => new \HubSpot\Client\Crm\Deals\Model\BatchReadInputSimplePublicObjectId(),
'inputClass' => \HubSpot\Client\Crm\Deals\Model\SimplePublicObjectId::class,
'api' => $this->getNewInstance()->crm()->deals()->batchApi(),
],
'companies' => [
'batchReadRequest' => new \HubSpot\Client\Crm\Companies\Model\BatchReadInputSimplePublicObjectId(),
'inputClass' => \HubSpot\Client\Crm\Companies\Model\SimplePublicObjectId::class,
'api' => $this->getNewInstance()->crm()->companies()->batchApi(),
],
'contacts' => [
'batchReadRequest' => new \HubSpot\Client\Crm\Contacts\Model\BatchReadInputSimplePublicObjectId(),
'inputClass' => \HubSpot\Client\Crm\Contacts\Model\SimplePublicObjectId::class,
'api' => $this->getNewInstance()->crm()->contacts()->batchApi(),
],
];
if (! isset($configurations[$objectType])) {
throw new \InvalidArgumentException("Unsupported object type: {$objectType}");
}
return $configurations[$objectType];
}
private function prepareBatchRequest(array $batchConfig, array $crmIds, array $fields): object
{
$batchReadRequest = $batchConfig['batchReadRequest'];
$inputClass = $batchConfig['inputClass'];
$inputs = array_map(function ($crmId) use ($inputClass) {
$input = new $inputClass();
$input->setId($crmId);
return $input;
}, $crmIds);
$batchReadRequest->setInputs($inputs);
$batchReadRequest->setProperties($fields);
return $batchReadRequest;
}
private function validateApiResponse($response, string $objectType): void
{
if (! $response) {
throw new CrmException("HubSpot API returned null response for {$objectType} batch read");
}
}
private function processApiResults($response): array
{
$results = [];
$responseResults = $response->getResults();
if ($responseResults) {
foreach ($responseResults as $object) {
if ($object && $object->getId()) {
$results[$object->getId()] = [
'id' => $object->getId(),
'properties' => $object->getProperties() ?: [],
];
}
}
}
return $results;
}
private function logBatchResults(string $objectType, array $crmIds, array $results): void
{
$this->log->info("[HubSpot] Batch fetched {$objectType}", [
'requested_count' => count($crmIds),
'returned_count' => count($results),
'crm_ids' => $crmIds,
]);
}
private function handleBatchError(\Throwable $e, string $objectType, array $crmIds): void
{
$errorMessage = $e->getMessage() ?: 'Unknown error';
$errorTrace = $e->getTraceAsString() ?: 'No trace available';
$this->log->error("[HubSpot] Failed to batch fetch {$objectType}", [
'crm_ids' => $crmIds,
'error' => $errorMessage,
'trace' => $errorTrace,
]);
throw new CrmException("Failed to batch fetch {$objectType}: " . $errorMessage);
}
/**
* Batch read multiple opportunities by their CRM IDs
*
* @param array<string> $crmIds Array of HubSpot deal IDs (max 100)
* @param array<string> $fields Array of property names to fetch
*
* @return array<string, array> Array keyed by CRM ID with opportunity data
*/
public function getOpportunitiesByIds(array $crmIds, array $fields): array
{
return $this->batchReadObjects('deals', $crmIds, $fields);
}
/**
* Batch read multiple companies by their CRM IDs
*
* @param array<string> $crmIds Array of HubSpot company IDs (max 100)
* @param array<string> $fields Array of property names to fetch
*
* @return array<string, array> Array keyed by CRM ID with company data
*/
public function getCompaniesByIds(array $crmIds, array $fields): array
{
return $this->batchReadObjects('companies', $crmIds, $fields);
}
/**
* Batch read multiple contacts by their CRM IDs
*
* @param array<string> $crmIds Array of HubSpot contact IDs (max 100)
* @param array<string> $fields Array of property names to fetch
*
* @return array<string, array> Array keyed by CRM ID with contact data
*/
public function getContactsByIds(array $crmIds, array $fields): array
{
return $this->batchReadObjects('contacts', $crmIds, $fields);
}
/**
* @throws CompanyApiException
* @throws CrmException
*/
public function getAccountById(string $crmId, array $fields): array
{
try {
$company = $this->getNewInstance()->crm()->companies()->basicApi()->getById(
$crmId,
implode(',', $fields),
);
} catch (CompanyApiException $e) {
$this->log->info('[Hubspot] Failed to fetch account', [
'crm_id' => $crmId,
'reason' => $e->getMessage(),
]);
throw $e;
}
if (! $company instanceof CompaniesWithAssociations) {
throw new CrmException('Account not found');
}
return [
'id' => $company->getId(),
'properties' => $company->getProperties(),
];
}
/**
* @throws ContactApiException
* @throws CrmException
*/
public function getContactById(string $crmId, array $fields): array
{
try {
$contact = $this->getNewInstance()->crm()->contacts()->basicApi()->getById(
$crmId,
implode(',', $fields)
);
} catch (ContactApiException $e) {
$this->log->info('[Hubspot] Failed to fetch contact', [
'crm_id' => $crmId,
'reason' => $e->getMessage(),
]);
throw $e;
}
if (! $contact instanceof ContactsWithAssociations) {
throw new CrmException('Contact not found');
}
return [
'id' => $contact->getId(),
'properties' => $contact->getProperties(),
];
}
/**
* This is email search request that Hubspot offers as GET (more generous quota)
*/
public function getContactByEmail(string $email, array $fields = []): array
{
try {
$contact = $this->getNewInstance()->crm()->contacts()->basicApi()->getById(
$email,
implode(',', $fields),
null,
false,
'email'
);
return [
'id' => $contact->getId(),
'properties' => $contact->getProperties(),
];
} catch (ContactApiException $e) {
$this->log->info('[Hubspot] Failed to fetch contact', [
'email' => $email,
'reason' => $e->getMessage(),
]);
return [];
}
}
/**
* @throws CrmException
*/
public function fetchProperty(string $objectType, string $propertyId): Property
{
$result = $this->getNewInstance()->crm()->properties()->coreApi()->getByName($objectType, $propertyId);
if (! $result instanceof Property) {
$this->log->error('[Hubspot] Failed to fetch property', [
'object_type' => $objectType,
'property_id' => $propertyId,
'reason' => $result->getMessage(),
]);
throw new CrmException('Failed to fetch property');
}
return $result;
}
/**
* @return array<CrmFieldOption>
*/
public function fetchPropertyOptions(string $objectType, string $propertyId): array
{
/** @var array<CrmFieldOption> */
return $this->fetchProperty($objectType, $propertyId)->getOptions();
}
/**
* @return array<array{id:string, label:string, deleted:bool}>
*/
public function fetchCallDispositions(): array
{
/** @var Response $response */
$response = $this->getInstance()->engagements()->getCallDispositions();
/**
* @var array<array{
* id:string,
* label:string,
* deleted: bool
* }>
*/
return $response->toArray();
}
/**
* @return array<CrmFieldOption>
*/
public function fetchOpportunityPipelineStages(): array
{
$stages = [];
$apiResponse = $this->getNewInstance()->crm()->pipelines()->pipelinesApi()->getAll('deals');
if ($apiResponse instanceof Error) {
$this->log->error('[Hubspot] Failed to fetch opportunity pipelines', [
'reason' => $apiResponse->getMessage(),
]);
return [];
}
foreach ($apiResponse->getResults() as $pipeline) {
$pipelineStages = array_map(
static function (PipelineStage $stage) {
return [
'id' => $stage->getId(),
'label' => $stage->getLabel(),
];
},
$pipeline->getStages()
);
$stages = array_merge($stages, $pipelineStages);
}
return $stages;
}
public function fetchOpportunityPipelines(): array
{
$pipelines = [];
try {
$apiResponse = $this->makeRequest('/crm/v3/pipelines/deals');
} catch (\Exception $e) {
$this->log->info('[Hubspot] Failed to fetch opportunity pipelines', [
'reason' => $e->getMessage(),
]);
return [];
}
$response = $apiResponse->toArray();
foreach ($response['results'] as $pipeline) {
$pipelines[] = [
'id' => $pipeline['id'],
'label' => $pipeline['label'],
];
}
return $pipelines;
}
/**
* @return array<CrmFieldOption>
*/
public function fetchMeetingOutcomeFieldOptions(Field $field): array
{
return $field->getCrmProviderId() === 'meetingOutcome'
? $this->fetchMeetingOutcomeTypes()
: $this->fetchCallActivityTypes();
}
public function fetchMeetingOutcomeTypes(): array
{
return $this->extractMeetingTypeOptions(
'[URL_WITH_CREDENTIALS] Response $response */
$response = $this->getInstance()
->getClient()
->request('GET', $endpoint);
/**
* @var array<array{
* value: string,
* label: string,
* displayOrder: int
* }> $optionData
*/
$optionData = $response->toArray()['options'] ?? [];
$options = [];
foreach ($optionData as $item) {
$options[] = [
'id' => $item['value'],
'value' => $item['value'],
'label' => $item['label'],
'display_order' => $item['displayOrder'],
];
}
return $options;
}
/**
* @return array<CrmFieldOption>
*/
public function fetchDispositionFieldOptions(): array
{
$options = [];
$dispositions = $this->fetchCallDispositions();
foreach ($dispositions as $disposition) {
if ($disposition['deleted'] !== false) {
continue;
}
$option['value'] = $disposition['id'];
$option['id'] = $disposition['id'];
$option['label'] = $disposition['label'];
$options[] = $option;
}
return $options;
}
/**
* @return array<CrmFieldOption>
*/
public function fetchOpportunityFieldOptions(Field $field): array
{
if ($field->isStageField()) {
return $this->fetchOpportunityPipelineStages();
}
if ($field->isPipelineField()) {
return $this->fetchOpportunityPipelines();
}
return $this->fetchPropertyOptions('deals', $field->getCrmProviderId());
}
/**
* @throws BadRequest
* @throws HubspotException
*/
public function makeRequest(string $endpoint, $method = 'GET', $payload = [], ?string $queryString = null)
{
$endpoint = self::BASE_URL . $endpoint;
if ($method === 'GET') {
$response = $this->getInstance()->getClient()?->request(
method: $method,
endpoint: $endpoint,
query_string: $queryString
);
} else {
return $this->getInstance()->getClient()->request($method, $endpoint, [
'json' => ($payload),
]);
}
return $
}
/**
* @throws BadRequest
* @throws HubspotException
*/
public function createMeeting(array $payload): Response
{
$endpoint = '/crm/v3/objects/meetings';
return $this->makeRequest($endpoint, 'POST', $payload);
}
/**
* @throws BadRequest
* @throws HubspotException
*/
public function updateMeeting(string $meetingId, array $payload): Response
{
$endpoint = '/crm/v3/objects/meetings/' . $meetingId;
return $this->makeRequest($endpoint, 'PATCH', $payload);
}
/**
* @throws \Exception
*/
public function createNote(
string $body,
string $ownerId,
int $timestamp,
string $objectId,
NoteObject $noteObject
): ?string {
try {
$noteInput = new SimplePublicObjectInput([
'properties' => [
'hs_note_body' => $body,
'hubspot_owner_id' => $ownerId,
'hs_timestamp' => $timestamp,
],
]);
// Create note
$note = $this->getNewInstance()->crm()->objects()->basicApi()->create('note', $noteInput);
$this->getNewInstance()->crm()->objects()->associationsApi()->create(
'note',
$note->getId(),
$this->getNoteObject($noteObject),
$objectId,
$this->getNoteAssociationType($noteObject),
);
return $note->getId();
} catch (\Exception $e) {
$this->log->error('[Hubspot] Failed to create note', [
'objectId' => $objectId,
'noteObject' => $noteObject->getObjectType(),
'reason' => $e->getMessage(),
]);
\Sentry::captureException($e);
}
return null;
}
public function updateEngagement(string $objectId, array $engagement, array $metadata): void
{
$this->getInstance()->engagements()->update($objectId, $engagement, $metadata);
}
public function getEngagementData(string $engagementId): array
{
$engagement = $this->getInstance()->engagements()->get($engagementId);
return $engagement->toArray();
}
public function createEngagement(array $engagement, array $associations, array $metadata): Response
{
return $this->getInstance()
->engagements()
->create($engagement, $associations, $metadata);
}
public function isUnauthorizedException(\Exception $e): bool
{
// Check for specific HubSpot API exception types first
if ($e instanceof BadRequest) {
// BadRequest can contain 401 status codes
return $e->getCode() === 401;
}
// Check for HTTP client exceptions with status codes
if ($e instanceof \GuzzleHttp\Exception\RequestException && $e->hasResponse()) {
$response = $e->getResponse();
if ($response !== null) {
return $response->getStatusCode() === 401;
}
}
// Check for Guzzle HTTP exceptions
if ($e instanceof \GuzzleHttp\Exception\ClientException) {
return $e->getCode() === 401;
}
// Fallback to string matching as last resort, but be more specific
$message = strtolower($e->getMessage());
return str_contains($message, '401 unauthorized') ||
str_contains($message, 'http 401') ||
str_contains($message, 'status code 401') ||
(preg_match('/\b401\b/', $message) && str_contains($message, 'unauthorized'));
}
/**
* Validates and refreshes the access token if needed before API requests.
* This ensures long-running processes don't fail due to token expiration.
*
* @throws SocialAccountTokenInvalidException
*/
public function ensureValidToken(): void
{
if ($this->oauthAccount === null) {
return;
}
$newToken = $this->tokenManager->ensureValidToken($this->oauthAccount);
if ($newToken !== null) {
$this->accessToken = $newToken;
}
}
public function getConfig()
{
return $this->config;
}
// returns only active (archived=false)
public function getOwners(): array
{
return $this->getNewInstance()->crm()->owners()->getAll();
}
/**
* @param bool $archived
*
* @return array<Owner>|[]
*/
public function getOwnersArchived(bool $archived = true): array
{
$endpoint = '/crm/v3/owners';
$queryParams = [
'archived' => $archived ? 'true' : 'false',
];
$queryString = http_build_query($queryParams);
$owners = [];
try {
$response = $this->makeRequest(endpoint: $endpoint, queryString: $queryString);
$responseData = $response?->toArray();
foreach ($responseData['results'] as $result) {
try {
$owners[] = Owner::create($result);
} catch (Throwable $e) {
$this->log->error('[HubSpot] Failed to process owner data', [
'result' => $result,
'error' => $e->getMessage(),
]);
continue;
}
}
} catch (Throwable $e) {
$this->log->error('HubSpot] Failed to fetch owners', [
'archived' => $archived,
'error' => $e->getMessage(),
]);
return [];
}
return $owners;
}
public function getMeeting(string $engagementId): ObjectWithAssociations
{
return $this->getNewInstance()->crm()->objects()->basicApi()
->getById('meeting', $engagementId, null, 'contact,company,deal');
}
public function deleteEngagement(string $engagementId): void
{
$this->getInstance()->engagements()->delete((int) $engagementId);
}
public function getAssociationsData(array $ids, string $fromObject, string $toObject): array
{
$associationData = [];
$idChunks = array_chunk($ids, self::ASSOCIATIONS_BATCH_SIZE_LIMIT);
foreach ($idChunks as $idChunk) {
try {
$batchInput = new \HubSpot\Client\Crm\Associations\Model\BatchInputPublicObjectId();
$batchInput->setInputs(array_map(function ($id) {
$publicObjectId = new \HubSpot\Client\Crm\Associations\Model\PublicObjectId();
$publicObjectId->setId($id);
return $publicObjectId;
}, $idChunk));
$associatedObjectsData = $this
->getNewInstance()
->crm()
->associations()
->batchApi()
->read($fromObject, $toObject, $batchInput);
if ($associatedObjectsData instanceof \HubSpot\Client\Crm\Associations\Model\BatchResponsePublicAssociationMulti) {
foreach ($associatedObjectsData->getResults() as $association) {
$from = $association->getFrom()->getId();
$toAssociations = $association->getTo();
if (! empty($toAssociations)) {
$associationData[$from] = array_map(function ($item) {
return $item->getId();
}, $toAssociations);
}
}
}
} catch (\Exception $e) {
$this->log->error('[Hubspot] Failed to fetch associations', [
'from_object' => $fromObject,
'to_object' => $toObject,
'reason' => $e->getMessage(),
]);
}
}
return $associationData;
}
/**
* @throws \Exception
*/
private function getNoteAssociationType(NoteObject $noteObject): string
{
return match($noteObject) {
NoteObject::Opportunity => 'note_to_deal',
NoteObject::Lead, NoteObject::Contact => 'note_to_contact', // or 'note_to_lead' if your portal supports it
NoteObject::Account => 'note_to_company',
NoteObject::Call, NoteObject::Event => throw new \Exception('Not supported'),
};
}
/**
* @throws \Exception
*/
private function getNoteObject(NoteObject $noteObject): string
{
return match($noteObject) {
NoteObject::Opportunity => 'deal',
NoteObject::Lead, NoteObject::Contact => 'contact',
NoteObject::Account => 'company',
NoteObject::Call, NoteObject::Event => throw new \Exception('Not supported'),
};
}
public function addAssociations(string $objectType, string $associationType, array $payload): Response
{
$endpoint = "/crm/v4/associations/$objectType/$associationType/batch/create";
return $this->makeRequest($endpoint, 'POST', $payload);
}
public function removeAssociations(string $objectType, string $associationType, array $payload): Response
{
$endpoint = "/crm/v4/associations/$objectType/$associationType/batch/archive";
return $this->makeRequest($endpoint, 'POST', $payload);
}
}
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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):...
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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...
|
Claude
|
Claude
|
NULL
|
2643
|
|
2644
|
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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?
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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')...
|
Claude
|
Claude
|
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# [PASSWORD_DOTS] HS [PASSWORD_DOTS]
select * from teams where id = 2; # 2
select * from features; # 2
select * from team_features where team_id = 2; # 2
select * from crm_configurations where id = 2; # 2
select * from users where team_id = 2; #
select * from playbooks where team_id = 2; # event 38
select * from playbook_categories where playbook_id = 38; #
SELECT * FROM activities WHERE crm_configuration_id = 2 and crm_provider_id is not null order by id desc;
[URL_WITH_CREDENTIALS] CrmFieldOption array{id:string, label:string, value?:string}
*/
class Client extends BaseClient implements HubspotClientInterface
{
public const string MIN_API_VERSION = '2';
public const string BASE_URL = '[URL_WITH_CREDENTIALS] T
* @param callable(): T $apiCall
* @return T
*
* @throws RateLimitException
*/
private function executeRequest(callable $apiCall)
{
if (! $this->rateLimiter->canMakeRequest($this->config)) {
$retryAfter = $this->rateLimiter->requestAvailableIn($this->config);
$this->log->warning('[Hubspot] Rate limit exceeded, deferring request', [
'team_id' => $this->config->team_id,
'config_id' => $this->config->getId(),
'retry_after' => $retryAfter,
]);
throw new RateLimitException(
'Hubspot rate limit reached for configuration ' . $this->config->getId(),
$retryAfter,
);
}
$this->rateLimiter->incrementRequestCount($this->config);
try {
return $apiCall();
} catch (Throwable $e) {
if ($this->isHubspotRateLimit($e)) {
$retryAfter = $this->parseRetryAfter($e);
$this->log->warning('[Hubspot] Received 429 from API', [
'team_id' => $this->config->team_id,
'config_id' => $this->config->getId(),
'retry_after' => $retryAfter,
'reason' => $e->getMessage(),
]);
throw new RateLimitException('Hubspot returned 429', $retryAfter, $e);
}
throw $e;
}
}
private function isHubspotRateLimit(Throwable $e): bool
{
return method_exists($e, 'getCode') && (int) $e->getCode() === 429;
}
private function parseRetryAfter(Throwable $e): int
{
if (method_exists($e, 'getResponseHeaders')) {
$headers = $e->getResponseHeaders() ?: [];
$value = $headers['Retry-After'] ?? $headers['retry-after'] ?? null;
if (is_array($value)) {
$value = $value[0] ?? null;
}
if (is_numeric($value)) {
return (int) $value;
}
}
return 10;
}
public function getMinimumApiVersion(): string
{
return self::MIN_API_VERSION;
}
public function getInstance(): Factory
{
return new Factory([
'key' => $this->accessToken,
'oauth2' => true,
'base_url' => $this->baseUrl,
]);
}
public function getNewInstance(): Discovery
{
return \HubSpot\Factory::createWithAccessToken($this->accessToken);
}
/**
* Secondly and daily limits for Hubspot API
*
* Product Tier: Free & Starter | Professional & Enterprise | API add-on (any tier)
* Burst: 100/10 seconds | 150/10 seconds | 200/10 seconds
* Daily: 250,000 | 500,000 | 1,000,000
*
* Official documentation states: The search endpoints are rate limited to five requests per second.
* Since with 5 RPS were still hitting secondly rate limits we lowered it to 4
*/
public function getPaginatedData(array $payload, string $type, int $offset = 0): array
{
$total = 0;
$lastId = null;
$rows = [];
foreach ($this->getPaginatedDataGenerator($payload, $type, $offset, $total, $lastId) as $row) {
$rows[] = $row;
}
return ['results' => $rows, 'total' => $total, 'last_record' => $lastId];
}
/**
* @throws HubspotException
* @throws SocialAccountTokenInvalidException
* @throws BadRequest
*/
public function getPaginatedDataGenerator(
array $payload,
string $type,
int $offset = 0,
int &$total = 0,
?string &$lastRecordId = null
): \Generator {
return $this->paginationService->getPaginatedDataGenerator(
$this,
$payload,
$type,
$offset,
$total,
$lastRecordId
);
}
/**
* @throws DealApiException
* @throws CrmException
*/
public function getOpportunityById(string $crmId, array $fields): array
{
try {
$deal = $this->executeRequest(fn () => $this->getNewInstance()->crm()->deals()->basicApi()->getById(
$crmId,
implode(',', $fields),
'companies,contacts'
));
} catch (DealApiException $e) {
$this->log->info('[Hubspot] Failed to fetch opportunity', [
'crm_id' => $crmId,
'reason' => $e->getMessage(),
]);
throw $e;
}
if (! $deal instanceof DealWithAssociations) {
throw new CrmException('Deal not found');
}
return [
'id' => $deal->getId(),
'properties' => $deal->getProperties(),
'associations' => $deal->getAssociations(),
];
}
/**
* Generic batch read method for HubSpot objects
*
* @param string $objectType The object type ('deals', 'companies', 'contacts')
* @param array<string> $crmIds Array of HubSpot object IDs (max 100)
* @param array<string> $fields Array of property names to fetch
*
* @return array<string, array> Array keyed by CRM ID with object data
*/
private function batchReadObjects(string $objectType, array $crmIds, array $fields): array
{
if (empty($crmIds)) {
return [];
}
$this->validateBatchSize($objectType, $crmIds);
$this->ensureValidToken();
try {
$batchConfig = $this->createBatchConfiguration($objectType);
$batchReadRequest = $this->prepareBatchRequest($batchConfig, $crmIds, $fields);
$response = $batchConfig['api']->read($batchReadRequest);
$this->validateApiResponse($response, $objectType);
$results = $this->processApiResults($response);
$this->logBatchResults($objectType, $crmIds, $results);
return $results;
} catch (\Throwable $e) {
$this->handleBatchError($e, $objectType, $crmIds);
}
}
private function validateBatchSize(string $objectType, array $crmIds): void
{
if (count($crmIds) > 100) {
throw new \InvalidArgumentException("Batch size cannot exceed 100 {$objectType}");
}
}
private function createBatchConfiguration(string $objectType): array
{
$configurations = [
'deals' => [
'batchReadRequest' => new \HubSpot\Client\Crm\Deals\Model\BatchReadInputSimplePublicObjectId(),
'inputClass' => \HubSpot\Client\Crm\Deals\Model\SimplePublicObjectId::class,
'api' => $this->getNewInstance()->crm()->deals()->batchApi(),
],
'companies' => [
'batchReadRequest' => new \HubSpot\Client\Crm\Companies\Model\BatchReadInputSimplePublicObjectId(),
'inputClass' => \HubSpot\Client\Crm\Companies\Model\SimplePublicObjectId::class,
'api' => $this->getNewInstance()->crm()->companies()->batchApi(),
],
'contacts' => [
'batchReadRequest' => new \HubSpot\Client\Crm\Contacts\Model\BatchReadInputSimplePublicObjectId(),
'inputClass' => \HubSpot\Client\Crm\Contacts\Model\SimplePublicObjectId::class,
'api' => $this->getNewInstance()->crm()->contacts()->batchApi(),
],
];
if (! isset($configurations[$objectType])) {
throw new \InvalidArgumentException("Unsupported object type: {$objectType}");
}
return $configurations[$objectType];
}
private function prepareBatchRequest(array $batchConfig, array $crmIds, array $fields): object
{
$batchReadRequest = $batchConfig['batchReadRequest'];
$inputClass = $batchConfig['inputClass'];
$inputs = array_map(function ($crmId) use ($inputClass) {
$input = new $inputClass();
$input->setId($crmId);
return $input;
}, $crmIds);
$batchReadRequest->setInputs($inputs);
$batchReadRequest->setProperties($fields);
return $batchReadRequest;
}
private function validateApiResponse($response, string $objectType): void
{
if (! $response) {
throw new CrmException("HubSpot API returned null response for {$objectType} batch read");
}
}
private function processApiResults($response): array
{
$results = [];
$responseResults = $response->getResults();
if ($responseResults) {
foreach ($responseResults as $object) {
if ($object && $object->getId()) {
$results[$object->getId()] = [
'id' => $object->getId(),
'properties' => $object->getProperties() ?: [],
];
}
}
}
return $results;
}
private function logBatchResults(string $objectType, array $crmIds, array $results): void
{
$this->log->info("[HubSpot] Batch fetched {$objectType}", [
'requested_count' => count($crmIds),
'returned_count' => count($results),
'crm_ids' => $crmIds,
]);
}
private function handleBatchError(\Throwable $e, string $objectType, array $crmIds): void
{
$errorMessage = $e->getMessage() ?: 'Unknown error';
$errorTrace = $e->getTraceAsString() ?: 'No trace available';
$this->log->error("[HubSpot] Failed to batch fetch {$objectType}", [
'crm_ids' => $crmIds,
'error' => $errorMessage,
'trace' => $errorTrace,
]);
throw new CrmException("Failed to batch fetch {$objectType}: " . $errorMessage);
}
/**
* Batch read multiple opportunities by their CRM IDs
*
* @param array<string> $crmIds Array of HubSpot deal IDs (max 100)
* @param array<string> $fields Array of property names to fetch
*
* @return array<string, array> Array keyed by CRM ID with opportunity data
*/
public function getOpportunitiesByIds(array $crmIds, array $fields): array
{
return $this->batchReadObjects('deals', $crmIds, $fields);
}
/**
* Batch read multiple companies by their CRM IDs
*
* @param array<string> $crmIds Array of HubSpot company IDs (max 100)
* @param array<string> $fields Array of property names to fetch
*
* @return array<string, array> Array keyed by CRM ID with company data
*/
public function getCompaniesByIds(array $crmIds, array $fields): array
{
return $this->batchReadObjects('companies', $crmIds, $fields);
}
/**
* Batch read multiple contacts by their CRM IDs
*
* @param array<string> $crmIds Array of HubSpot contact IDs (max 100)
* @param array<string> $fields Array of property names to fetch
*
* @return array<string, array> Array keyed by CRM ID with contact data
*/
public function getContactsByIds(array $crmIds, array $fields): array
{
return $this->batchReadObjects('contacts', $crmIds, $fields);
}
/**
* @throws CompanyApiException
* @throws CrmException
*/
public function getAccountById(string $crmId, array $fields): array
{
try {
$company = $this->getNewInstance()->crm()->companies()->basicApi()->getById(
$crmId,
implode(',', $fields),
);
} catch (CompanyApiException $e) {
$this->log->info('[Hubspot] Failed to fetch account', [
'crm_id' => $crmId,
'reason' => $e->getMessage(),
]);
throw $e;
}
if (! $company instanceof CompaniesWithAssociations) {
throw new CrmException('Account not found');
}
return [
'id' => $company->getId(),
'properties' => $company->getProperties(),
];
}
/**
* @throws ContactApiException
* @throws CrmException
*/
public function getContactById(string $crmId, array $fields): array
{
try {
$contact = $this->getNewInstance()->crm()->contacts()->basicApi()->getById(
$crmId,
implode(',', $fields)
);
} catch (ContactApiException $e) {
$this->log->info('[Hubspot] Failed to fetch contact', [
'crm_id' => $crmId,
'reason' => $e->getMessage(),
]);
throw $e;
}
if (! $contact instanceof ContactsWithAssociations) {
throw new CrmException('Contact not found');
}
return [
'id' => $contact->getId(),
'properties' => $contact->getProperties(),
];
}
/**
* This is email search request that Hubspot offers as GET (more generous quota)
*/
public function getContactByEmail(string $email, array $fields = []): array
{
try {
$contact = $this->getNewInstance()->crm()->contacts()->basicApi()->getById(
$email,
implode(',', $fields),
null,
false,
'email'
);
return [
'id' => $contact->getId(),
'properties' => $contact->getProperties(),
];
} catch (ContactApiException $e) {
$this->log->info('[Hubspot] Failed to fetch contact', [
'email' => $email,
'reason' => $e->getMessage(),
]);
return [];
}
}
/**
* @throws CrmException
*/
public function fetchProperty(string $objectType, string $propertyId): Property
{
$result = $this->getNewInstance()->crm()->properties()->coreApi()->getByName($objectType, $propertyId);
if (! $result instanceof Property) {
$this->log->error('[Hubspot] Failed to fetch property', [
'object_type' => $objectType,
'property_id' => $propertyId,
'reason' => $result->getMessage(),
]);
throw new CrmException('Failed to fetch property');
}
return $result;
}
/**
* @return array<CrmFieldOption>
*/
public function fetchPropertyOptions(string $objectType, string $propertyId): array
{
/** @var array<CrmFieldOption> */
return $this->fetchProperty($objectType, $propertyId)->getOptions();
}
/**
* @return array<array{id:string, label:string, deleted:bool}>
*/
public function fetchCallDispositions(): array
{
/** @var Response $response */
$response = $this->getInstance()->engagements()->getCallDispositions();
/**
* @var array<array{
* id:string,
* label:string,
* deleted: bool
* }>
*/
return $response->toArray();
}
/**
* @return array<CrmFieldOption>
*/
public function fetchOpportunityPipelineStages(): array
{
$stages = [];
$apiResponse = $this->getNewInstance()->crm()->pipelines()->pipelinesApi()->getAll('deals');
if ($apiResponse instanceof Error) {
$this->log->error('[Hubspot] Failed to fetch opportunity pipelines', [
'reason' => $apiResponse->getMessage(),
]);
return [];
}
foreach ($apiResponse->getResults() as $pipeline) {
$pipelineStages = array_map(
static function (PipelineStage $stage) {
return [
'id' => $stage->getId(),
'label' => $stage->getLabel(),
];
},
$pipeline->getStages()
);
$stages = array_merge($stages, $pipelineStages);
}
return $stages;
}
public function fetchOpportunityPipelines(): array
{
$pipelines = [];
try {
$apiResponse = $this->makeRequest('/crm/v3/pipelines/deals');
} catch (\Exception $e) {
$this->log->info('[Hubspot] Failed to fetch opportunity pipelines', [
'reason' => $e->getMessage(),
]);
return [];
}
$response = $apiResponse->toArray();
foreach ($response['results'] as $pipeline) {
$pipelines[] = [
'id' => $pipeline['id'],
'label' => $pipeline['label'],
];
}
return $pipelines;
}
/**
* @return array<CrmFieldOption>
*/
public function fetchMeetingOutcomeFieldOptions(Field $field): array
{
return $field->getCrmProviderId() === 'meetingOutcome'
? $this->fetchMeetingOutcomeTypes()
: $this->fetchCallActivityTypes();
}
public function fetchMeetingOutcomeTypes(): array
{
return $this->extractMeetingTypeOptions(
'[URL_WITH_CREDENTIALS] Response $response */
$response = $this->getInstance()
->getClient()
->request('GET', $endpoint);
/**
* @var array<array{
* value: string,
* label: string,
* displayOrder: int
* }> $optionData
*/
$optionData = $response->toArray()['options'] ?? [];
$options = [];
foreach ($optionData as $item) {
$options[] = [
'id' => $item['value'],
'value' => $item['value'],
'label' => $item['label'],
'display_order' => $item['displayOrder'],
];
}
return $options;
}
/**
* @return array<CrmFieldOption>
*/
public function fetchDispositionFieldOptions(): array
{
$options = [];
$dispositions = $this->fetchCallDispositions();
foreach ($dispositions as $disposition) {
if ($disposition['deleted'] !== false) {
continue;
}
$option['value'] = $disposition['id'];
$option['id'] = $disposition['id'];
$option['label'] = $disposition['label'];
$options[] = $option;
}
return $options;
}
/**
* @return array<CrmFieldOption>
*/
public function fetchOpportunityFieldOptions(Field $field): array
{
if ($field->isStageField()) {
return $this->fetchOpportunityPipelineStages();
}
if ($field->isPipelineField()) {
return $this->fetchOpportunityPipelines();
}
return $this->fetchPropertyOptions('deals', $field->getCrmProviderId());
}
/**
* @throws BadRequest
* @throws HubspotException
*/
public function makeRequest(string $endpoint, $method = 'GET', $payload = [], ?string $queryString = null)
{
$endpoint = self::BASE_URL . $endpoint;
if ($method === 'GET') {
$response = $this->getInstance()->getClient()?->request(
method: $method,
endpoint: $endpoint,
query_string: $queryString
);
} else {
return $this->getInstance()->getClient()->request($method, $endpoint, [
'json' => ($payload),
]);
}
return $response;
}
/**
* @throws BadRequest
* @throws HubspotException
*/
public function createMeeting(array $payload): Response
{
$endpoint = '/crm/v3/objects/meetings';
return $this->makeRequest($endpoint, 'POST', $payload);
}
/**
* @throws BadRequest
* @throws HubspotException
*/
public function updateMeeting(string $meetingId, array $payload): Response
{
$endpoint = '/crm/v3/objects/meetings/' . $meetingId;
return $this->makeRequest($endpoint, 'PATCH', $payload);
}
/**
* @throws \Exception
*/
public function createNote(
string $body,
string $ownerId,
int $timestamp,
string $objectId,
NoteObject $noteObject
): ?string {
try {
$noteInput = new SimplePublicObjectInput([
'properties' => [
'hs_note_body' => $body,
'hubspot_owner_id' => $ownerId,
'hs_timestamp' => $timestamp,
],
]);
// Create note
$note = $this->getNewInstance()->crm()->objects()->basicApi()->create('note', $noteInput);
$this->getNewInstance()->crm()->objects()->associationsApi()->create(
'note',
$note->getId(),
$this->getNoteObject($noteObject),
$objectId,
$this->getNoteAssociationType($noteObject),
);
return $note->getId();
} catch (\Exception $e) {
$this->log->error('[Hubspot] Failed to create note', [
'objectId' => $objectId,
'noteObject' => $noteObject->getObjectType(),
'reason' => $e->getMessage(),
]);
\Sentry::captureException($e);
}
return null;
}
public function updateEngagement(string $objectId, array $engagement, array $metadata): void
{
$this->getInstance()->engagements()->update($objectId, $engagement, $metadata);
}
public function getEngagementData(string $engagementId): array
{
$engagement = $this->getInstance()->engagements()->get($engagementId);
return $engagement->toArray();
}
public function createEngagement(array $engagement, array $associations, array $metadata): Response
{
return $this->getInstance()
->engagements()
->create($engagement, $associations, $metadata);
}
public function isUnauthorizedException(\Exception $e): bool
{
// Check for specific HubSpot API exception types first
if ($e instanceof BadRequest) {
// BadRequest can contain 401 status codes
return $e->getCode() === 401;
}
// Check for HTTP client exceptions with status codes
if ($e instanceof \GuzzleHttp\Exception\RequestException && $e->hasResponse()) {
$response = $e->getResponse();
if ($response !== null) {
return $response->getStatusCode() === 401;
}
}
// Check for Guzzle HTTP exceptions
if ($e instanceof \GuzzleHttp\Exception\ClientException) {
return $e->getCode() === 401;
}
// Fallback to string matching as last resort, but be more specific
$message = strtolower($e->getMessage());
return str_contains($message, '401 unauthorized') ||
str_contains($message, 'http 401') ||
str_contains($message, 'status code 401') ||
(preg_match('/\b401\b/', $message) && str_contains($message, 'unauthorized'));
}
/**
* Validates and refreshes the access token if needed before API requests.
* This ensures long-running processes don't fail due to token expiration.
*
* @throws SocialAccountTokenInvalidException
*/
public function ensureValidToken(): void
{
if ($this->oauthAccount === null) {
return;
}
$newToken = $this->tokenManager->ensureValidToken($this->oauthAccount);
if ($newToken !== null) {
$this->accessToken = $newToken;
}
}
public function getConfig()
{
return $this->config;
}
// returns only active (archived=false)
public function getOwners(): array
{
return $this->getNewInstance()->crm()->owners()->getAll();
}
/**
* @param bool $archived
*
* @return array<Owner>|[]
*/
public function getOwnersArchived(bool $archived = true): array
{
$endpoint = '/crm/v3/owners';
$queryParams = [
'archived' => $archived ? 'true' : 'false',
];
$queryString = http_build_query($queryParams);
$owners = [];
try {
$response = $this->makeRequest(endpoint: $endpoint, queryString: $queryString);
$responseData = $response?->toArray();
foreach ($responseData['results'] as $result) {
try {
$owners[] = Owner::create($result);
} catch (Throwable $e) {
$this->log->error('[HubSpot] Failed to process owner data', [
'result' => $result,
'error' => $e->getMessage(),
]);
continue;
}
}
} catch (Throwable $e) {
$this->log->error('HubSpot] Failed to fetch owners', [
'archived' => $archived,
'error' => $e->getMessage(),
]);
return [];
}
return $owners;
}
public function getMeeting(string $engagementId): ObjectWithAssociations
{
return $this->getNewInstance()->crm()->objects()->basicApi()
->getById('meeting', $engagementId, null, 'contact,company,deal');
}
public function deleteEngagement(string $engagementId): void
{
$this->getInstance()->engagements()->delete((int) $engagementId);
}
public function getAssociationsData(array $ids, string $fromObject, string $toObject): array
{
$associationData = [];
$idChunks = array_chunk($ids, self::ASSOCIATIONS_BATCH_SIZE_LIMIT);
foreach ($idChunks as $idChunk) {
try {
$batchInput = new \HubSpot\Client\Crm\Associations\Model\BatchInputPublicObjectId();
$batchInput->setInputs(array_map(function ($id) {
$publicObjectId = new \HubSpot\Client\Crm\Associations\Model\PublicObjectId();
$publicObjectId->setId($id);
return $publicObjectId;
}, $idChunk));
$associatedObjectsData = $this
->getNewInstance()
->crm()
->associations()
->batchApi()
->read($fromObject, $toObject, $batchInput);
if ($associatedObjectsData instanceof \HubSpot\Client\Crm\Associations\Model\BatchResponsePublicAssociationMulti) {
foreach ($associatedObjectsData->getResults() as $association) {
$from = $association->getFrom()->getId();
$toAssociations = $association->getTo();
if (! empty($toAssociations)) {
$associationData[$from] = array_map(function ($item) {
return $item->getId();
}, $toAssociations);
}
}
}
} catch (\Exception $e) {
$this->log->error('[Hubspot] Failed to fetch associations', [
'from_object' => $fromObject,
'to_object' => $toObject,
'reason' => $e->getMessage(),
]);
}
}
return $associationData;
}
/**
* @throws \Exception
*/
private function getNoteAssociationType(NoteObject $noteObject): string
{
return match($noteObject) {
NoteObject::Opportunity => 'note_to_deal',
NoteObject::Lead, NoteObject::Contact => 'note_to_contact', // or 'note_to_lead' if your portal supports it
NoteObject::Account => 'note_to_company',
NoteObject::Call, NoteObject::Event => throw new \Exception('Not supported'),
};
}
/**
* @throws \Exception
*/
private function getNoteObject(NoteObject $noteObject): string
{
return match($noteObject) {
NoteObject::Opportunity => 'deal',
NoteObject::Lead, NoteObject::Contact => 'contact',
NoteObject::Account => 'company',
NoteObject::Call, NoteObject::Event => throw new \Exception('Not supported'),
};
}
public function addAssociations(string $objectType, string $associationType, array $payload): Response
{
$endpoint = "/crm/v4/associations/$objectType/$associationType/batch/create";
return $this->makeRequest($endpoint, 'POST', $payload);
}
public function removeAssociations(string $objectType, string $associationType, array $payload): Response
{
$endpoint = "/crm/v4/associations/$objectType/$associationType/batch/archive";
return $this->makeRequest($endpoint, 'POST', $payload);
}
}
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# [PASSWORD_DOTS] HS [PASSWORD_DOTS]
select * from teams where id = 2; # 2
select * from features; # 2
select * from team_features where team_id = 2; # 2
select * from crm_configurations where id = 2; # 2
select * from users where team_id = 2; #
select * from playbooks where team_id = 2; # event 38
select * from playbook_categories where playbook_id = 38; #
SELECT * FROM activities WHERE crm_configuration_id = 2 and crm_provider_id is not null order by id desc;
[URL_WITH_CREDENTIALS] CrmFieldOption array{id:string, label:string, value?:string}
*/
class Client extends BaseClient implements HubspotClientInterface
{
public const string MIN_API_VERSION = '2';
public const string BASE_URL = '[URL_WITH_CREDENTIALS] T
* @param callable(): T $apiCall
* @return T
*
* @throws RateLimitException
*/
private function executeRequest(callable $apiCall)
{
if (! $this->rateLimiter->canMakeRequest($this->config)) {
$retryAfter = $this->rateLimiter->requestAvailableIn($this->config);
$this->log->warning('[Hubspot] Rate limit exceeded, deferring request', [
'team_id' => $this->config->team_id,
'config_id' => $this->config->getId(),
'retry_after' => $retryAfter,
]);
throw new RateLimitException(
'Hubspot rate limit reached for configuration ' . $this->config->getId(),
$retryAfter,
);
}
$this->rateLimiter->incrementRequestCount($this->config);
try {
return $apiCall();
} catch (Throwable $e) {
if ($this->isHubspotRateLimit($e)) {
$retryAfter = $this->parseRetryAfter($e);
$this->log->warning('[Hubspot] Received 429 from API', [
'team_id' => $this->config->team_id,
'config_id' => $this->config->getId(),
'retry_after' => $retryAfter,
'reason' => $e->getMessage(),
]);
throw new RateLimitException('Hubspot returned 429', $retryAfter, $e);
}
throw $e;
}
}
private function isHubspotRateLimit(Throwable $e): bool
{
return method_exists($e, 'getCode') && (int) $e->getCode() === 429;
}
private function parseRetryAfter(Throwable $e): int
{
if (method_exists($e, 'getResponseHeaders')) {
$headers = $e->getResponseHeaders() ?: [];
$value = $headers['Retry-After'] ?? $headers['retry-after'] ?? null;
if (is_array($value)) {
$value = $value[0] ?? null;
}
if (is_numeric($value)) {
return (int) $value;
}
}
return 10;
}
public function getMinimumApiVersion(): string
{
return self::MIN_API_VERSION;
}
public function getInstance(): Factory
{
return new Factory([
'key' => $this->accessToken,
'oauth2' => true,
'base_url' => $this->baseUrl,
]);
}
public function getNewInstance(): Discovery
{
return \HubSpot\Factory::createWithAccessToken($this->accessToken);
}
/**
* Secondly and daily limits for Hubspot API
*
* Product Tier: Free & Starter | Professional & Enterprise | API add-on (any tier)
* Burst: 100/10 seconds | 150/10 seconds | 200/10 seconds
* Daily: 250,000 | 500,000 | 1,000,000
*
* Official documentation states: The search endpoints are rate limited to five requests per second.
* Since with 5 RPS were still hitting secondly rate limits we lowered it to 4
*/
public function getPaginatedData(array $payload, string $type, int $offset = 0): array
{
$total = 0;
$lastId = null;
$rows = [];
foreach ($this->getPaginatedDataGenerator($payload, $type, $offset, $total, $lastId) as $row) {
$rows[] = $row;
}
return ['results' => $rows, 'total' => $total, 'last_record' => $lastId];
}
/**
* @throws HubspotException
* @throws SocialAccountTokenInvalidException
* @throws BadRequest
*/
public function getPaginatedDataGenerator(
array $payload,
string $type,
int $offset = 0,
int &$total = 0,
?string &$lastRecordId = null
): \Generator {
return $this->paginationService->getPaginatedDataGenerator(
$this,
$payload,
$type,
$offset,
$total,
$lastRecordId
);
}
/**
* @throws DealApiException
* @throws CrmException
*/
public function getOpportunityById(string $crmId, array $fields): array
{
try {
$deal = $this->executeRequest(fn () => $this->getNewInstance()->crm()->deals()->basicApi()->getById(
$crmId,
implode(',', $fields),
'companies,contacts'
));
} catch (DealApiException $e) {
$this->log->info('[Hubspot] Failed to fetch opportunity', [
'crm_id' => $crmId,
'reason' => $e->getMessage(),
]);
throw $e;
}
if (! $deal instanceof DealWithAssociations) {
throw new CrmException('Deal not found');
}
return [
'id' => $deal->getId(),
'properties' => $deal->getProperties(),
'associations' => $deal->getAssociations(),
];
}
/**
* Generic batch read method for HubSpot objects
*
* @param string $objectType The object type ('deals', 'companies', 'contacts')
* @param array<string> $crmIds Array of HubSpot object IDs (max 100)
* @param array<string> $fields Array of property names to fetch
*
* @return array<string, array> Array keyed by CRM ID with object data
*/
private function batchReadObjects(string $objectType, array $crmIds, array $fields): array
{
if (empty($crmIds)) {
return [];
}
$this->validateBatchSize($objectType, $crmIds);
$this->ensureValidToken();
try {
$batchConfig = $this->createBatchConfiguration($objectType);
$batchReadRequest = $this->prepareBatchRequest($batchConfig, $crmIds, $fields);
$response = $batchConfig['api']->read($batchReadRequest);
$this->validateApiResponse($response, $objectType);
$results = $this->processApiResults($response);
$this->logBatchResults($objectType, $crmIds, $results);
return $results;
} catch (\Throwable $e) {
$this->handleBatchError($e, $objectType, $crmIds);
}
}
private function validateBatchSize(string $objectType, array $crmIds): void
{
if (count($crmIds) > 100) {
throw new \InvalidArgumentException("Batch size cannot exceed 100 {$objectType}");
}
}
private function createBatchConfiguration(string $objectType): array
{
$configurations = [
'deals' => [
'batchReadRequest' => new \HubSpot\Client\Crm\Deals\Model\BatchReadInputSimplePublicObjectId(),
'inputClass' => \HubSpot\Client\Crm\Deals\Model\SimplePublicObjectId::class,
'api' => $this->getNewInstance()->crm()->deals()->batchApi(),
],
'companies' => [
'batchReadRequest' => new \HubSpot\Client\Crm\Companies\Model\BatchReadInputSimplePublicObjectId(),
'inputClass' => \HubSpot\Client\Crm\Companies\Model\SimplePublicObjectId::class,
'api' => $this->getNewInstance()->crm()->companies()->batchApi(),
],
'contacts' => [
'batchReadRequest' => new \HubSpot\Client\Crm\Contacts\Model\BatchReadInputSimplePublicObjectId(),
'inputClass' => \HubSpot\Client\Crm\Contacts\Model\SimplePublicObjectId::class,
'api' => $this->getNewInstance()->crm()->contacts()->batchApi(),
],
];
if (! isset($configurations[$objectType])) {
throw new \InvalidArgumentException("Unsupported object type: {$objectType}");
}
return $configurations[$objectType];
}
private function prepareBatchRequest(array $batchConfig, array $crmIds, array $fields): object
{
$batchReadRequest = $batchConfig['batchReadRequest'];
$inputClass = $batchConfig['inputClass'];
$inputs = array_map(function ($crmId) use ($inputClass) {
$input = new $inputClass();
$input->setId($crmId);
return $input;
}, $crmIds);
$batchReadRequest->setInputs($inputs);
$batchReadRequest->setProperties($fields);
return $batchReadRequest;
}
private function validateApiResponse($response, string $objectType): void
{
if (! $response) {
throw new CrmException("HubSpot API returned null response for {$objectType} batch read");
}
}
private function processApiResults($response): array
{
$results = [];
$responseResults = $response->getResults();
if ($responseResults) {
foreach ($responseResults as $object) {
if ($object && $object->getId()) {
$results[$object->getId()] = [
'id' => $object->getId(),
'properties' => $object->getProperties() ?: [],
];
}
}
}
return $results;
}
private function logBatchResults(string $objectType, array $crmIds, array $results): void
{
$this->log->info("[HubSpot] Batch fetched {$objectType}", [
'requested_count' => count($crmIds),
'returned_count' => count($results),
'crm_ids' => $crmIds,
]);
}
private function handleBatchError(\Throwable $e, string $objectType, array $crmIds): void
{
$errorMessage = $e->getMessage() ?: 'Unknown error';
$errorTrace = $e->getTraceAsString() ?: 'No trace available';
$this->log->error("[HubSpot] Failed to batch fetch {$objectType}", [
'crm_ids' => $crmIds,
'error' => $errorMessage,
'trace' => $errorTrace,
]);
throw new CrmException("Failed to batch fetch {$objectType}: " . $errorMessage);
}
/**
* Batch read multiple opportunities by their CRM IDs
*
* @param array<string> $crmIds Array of HubSpot deal IDs (max 100)
* @param array<string> $fields Array of property names to fetch
*
* @return array<string, array> Array keyed by CRM ID with opportunity data
*/
public function getOpportunitiesByIds(array $crmIds, array $fields): array
{
return $this->batchReadObjects('deals', $crmIds, $fields);
}
/**
* Batch read multiple companies by their CRM IDs
*
* @param array<string> $crmIds Array of HubSpot company IDs (max 100)
* @param array<string> $fields Array of property names to fetch
*
* @return array<string, array> Array keyed by CRM ID with company data
*/
public function getCompaniesByIds(array $crmIds, array $fields): array
{
return $this->batchReadObjects('companies', $crmIds, $fields);
}
/**
* Batch read multiple contacts by their CRM IDs
*
* @param array<string> $crmIds Array of HubSpot contact IDs (max 100)
* @param array<string> $fields Array of property names to fetch
*
* @return array<string, array> Array keyed by CRM ID with contact data
*/
public function getContactsByIds(array $crmIds, array $fields): array
{
return $this->batchReadObjects('contacts', $crmIds, $fields);
}
/**
* @throws CompanyApiException
* @throws CrmException
*/
public function getAccountById(string $crmId, array $fields): array
{
try {
$company = $this->getNewInstance()->crm()->companies()->basicApi()->getById(
$crmId,
implode(',', $fields),
);
} catch (CompanyApiException $e) {
$this->log->info('[Hubspot] Failed to fetch account', [
'crm_id' => $crmId,
'reason' => $e->getMessage(),
]);
throw $e;
}
if (! $company instanceof CompaniesWithAssociations) {
throw new CrmException('Account not found');
}
return [
'id' => $company->getId(),
'properties' => $company->getProperties(),
];
}
/**
* @throws ContactApiException
* @throws CrmException
*/
public function getContactById(string $crmId, array $fields): array
{
try {
$contact = $this->getNewInstance()->crm()->contacts()->basicApi()->getById(
$crmId,
implode(',', $fields)
);
} catch (ContactApiException $e) {
$this->log->info('[Hubspot] Failed to fetch contact', [
'crm_id' => $crmId,
'reason' => $e->getMessage(),
]);
throw $e;
}
if (! $contact instanceof ContactsWithAssociations) {
throw new CrmException('Contact not found');
}
return [
'id' => $contact->getId(),
'properties' => $contact->getProperties(),
];
}
/**
* This is email search request that Hubspot offers as GET (more generous quota)
*/
public function getContactByEmail(string $email, array $fields = []): array
{
try {
$contact = $this->getNewInstance()->crm()->contacts()->basicApi()->getById(
$email,
implode(',', $fields),
null,
false,
'email'
);
return [
'id' => $contact->getId(),
'properties' => $contact->getProperties(),
];
} catch (ContactApiException $e) {
$this->log->info('[Hubspot] Failed to fetch contact', [
'email' => $email,
'reason' => $e->getMessage(),
]);
return [];
}
}
/**
* @throws CrmException
*/
public function fetchProperty(string $objectType, string $propertyId): Property
{
$result = $this->getNewInstance()->crm()->properties()->coreApi()->getByName($objectType, $propertyId);
if (! $result instanceof Property) {
$this->log->error('[Hubspot] Failed to fetch property', [
'object_type' => $objectType,
'property_id' => $propertyId,
'reason' => $result->getMessage(),
]);
throw new CrmException('Failed to fetch property');
}
return $result;
}
/**
* @return array<CrmFieldOption>
*/
public function fetchPropertyOptions(string $objectType, string $propertyId): array
{
/** @var array<CrmFieldOption> */
return $this->fetchProperty($objectType, $propertyId)->getOptions();
}
/**
* @return array<array{id:string, label:string, deleted:bool}>
*/
public function fetchCallDispositions(): array
{
/** @var Response $response */
$response = $this->getInstance()->engagements()->getCallDispositions();
/**
* @var array<array{
* id:string,
* label:string,
* deleted: bool
* }>
*/
return $response->toArray();
}
/**
* @return array<CrmFieldOption>
*/
public function fetchOpportunityPipelineStages(): array
{
$stages = [];
$apiResponse = $this->getNewInstance()->crm()->pipelines()->pipelinesApi()->getAll('deals');
if ($apiResponse instanceof Error) {
$this->log->error('[Hubspot] Failed to fetch opportunity pipelines', [
'reason' => $apiResponse->getMessage(),
]);
return [];
}
foreach ($apiResponse->getResults() as $pipeline) {
$pipelineStages = array_map(
static function (PipelineStage $stage) {
return [
'id' => $stage->getId(),
'label' => $stage->getLabel(),
];
},
$pipeline->getStages()
);
$stages = array_merge($stages, $pipelineStages);
}
return $stages;
}
public function fetchOpportunityPipelines(): array
{
$pipelines = [];
try {
$apiResponse = $this->makeRequest('/crm/v3/pipelines/deals');
} catch (\Exception $e) {
$this->log->info('[Hubspot] Failed to fetch opportunity pipelines', [
'reason' => $e->getMessage(),
]);
return [];
}
$response = $apiResponse->toArray();
foreach ($response['results'] as $pipeline) {
$pipelines[] = [
'id' => $pipeline['id'],
'label' => $pipeline['label'],
];
}
return $pipelines;
}
/**
* @return array<CrmFieldOption>
*/
public function fetchMeetingOutcomeFieldOptions(Field $field): array
{
return $field->getCrmProviderId() === 'meetingOutcome'
? $this->fetchMeetingOutcomeTypes()
: $this->fetchCallActivityTypes();
}
public function fetchMeetingOutcomeTypes(): array
{
return $this->extractMeetingTypeOptions(
'[URL_WITH_CREDENTIALS] Response $response */
$response = $this->getInstance()
->getClient()
->request('GET', $endpoint);
/**
* @var array<array{
* value: string,
* label: string,
* displayOrder: int
* }> $optionData
*/
$optionData = $response->toArray()['options'] ?? [];
$options = [];
foreach ($optionData as $item) {
$options[] = [
'id' => $item['value'],
'value' => $item['value'],
'label' => $item['label'],
'display_order' => $item['displayOrder'],
];
}
return $options;
}
/**
* @return array<CrmFieldOption>
*/
public function fetchDispositionFieldOptions(): array
{
$options = [];
$dispositions = $this->fetchCallDispositions();
foreach ($dispositions as $disposition) {
if ($disposition['deleted'] !== false) {
continue;
}
$option['value'] = $disposition['id'];
$option['id'] = $disposition['id'];
$option['label'] = $disposition['label'];
$options[] = $option;
}
return $options;
}
/**
* @return array<CrmFieldOption>
*/
public function fetchOpportunityFieldOptions(Field $field): array
{
if ($field->isStageField()) {
return $this->fetchOpportunityPipelineStages();
}
if ($field->isPipelineField()) {
return $this->fetchOpportunityPipelines();
}
return $this->fetchPropertyOptions('deals', $field->getCrmProviderId());
}
/**
* @throws BadRequest
* @throws HubspotException
*/
public function makeRequest(string $endpoint, $method = 'GET', $payload = [], ?string $queryString = null)
{
$endpoint = self::BASE_URL . $endpoint;
if ($method === 'GET') {
$response = $this->getInstance()->getClient()?->request(
method: $method,
endpoint: $endpoint,
query_string: $queryString
);
} else {
return $this->getInstance()->getClient()->request($method, $endpoint, [
'json' => ($payload),
]);
}
return $response;
}
/**
* @throws BadRequest
* @throws HubspotException
*/
public function createMeeting(array $payload): Response
{
$endpoint = '/crm/v3/objects/meetings';
return $this->makeRequest($endpoint, 'POST', $payload);
}
/**
* @throws BadRequest
* @throws HubspotException
*/
public function updateMeeting(string $meetingId, array $payload): Response
{
$endpoint = '/crm/v3/objects/meetings/' . $meetingId;
return $this->makeRequest($endpoint, 'PATCH', $payload);
}
/**
* @throws \Exception
*/
public function createNote(
string $body,
string $ownerId,
int $timestamp,
string $objectId,
NoteObject $noteObject
): ?string {
try {
$noteInput = new SimplePublicObjectInput([
'properties' => [
'hs_note_body' => $body,
'hubspot_owner_id' => $ownerId,
'hs_timestamp' => $timestamp,
],
]);
// Create note
$note = $this->getNewInstance()->crm()->objects()->basicApi()->create('note', $noteInput);
$this->getNewInstance()->crm()->objects()->associationsApi()->create(
'note',
$note->getId(),
$this->getNoteObject($noteObject),
$objectId,
$this->getNoteAssociationType($noteObject),
);
return $note->getId();
} catch (\Exception $e) {
$this->log->error('[Hubspot] Failed to create note', [
'objectId' => $objectId,
'noteObject' => $noteObject->getObjectType(),
'reason' => $e->getMessage(),
]);
\Sentry::captureException($e);
}
return null;
}
public function updateEngagement(string $objectId, array $engagement, array $metadata): void
{
$this->getInstance()->engagements()->update($objectId, $engagement, $metadata);
}
public function getEngagementData(string $engagementId): array
{
$engagement = $this->getInstance()->engagements()->get($engagementId);
return $engagement->toArray();
}
public function createEngagement(array $engagement, array $associations, array $metadata): Response
{
return $this->getInstance()
->engagements()
->create($engagement, $associations, $metadata);
}
public function isUnauthorizedException(\Exception $e): bool
{
// Check for specific HubSpot API exception types first
if ($e instanceof BadRequest) {
// BadRequest can contain 401 status codes
return $e->getCode() === 401;
}
// Check for HTTP client exceptions with status codes
if ($e instanceof \GuzzleHttp\Exception\RequestException && $e->hasResponse()) {
$response = $e->getResponse();
if ($response !== null) {
return $response->getStatusCode() === 401;
}
}
// Check for Guzzle HTTP exceptions
if ($e instanceof \GuzzleHttp\Exception\ClientException) {
return $e->getCode() === 401;
}
// Fallback to string matching as last resort, but be more specific
$message = strtolower($e->getMessage());
return str_contains($message, '401 unauthorized') ||
str_contains($message, 'http 401') ||
str_contains($message, 'status code 401') ||
(preg_match('/\b401\b/', $message) && str_contains($message, 'unauthorized'));
}
/**
* Validates and refreshes the access token if needed before API requests.
* This ensures long-running processes don't fail due to token expiration.
*
* @throws SocialAccountTokenInvalidException
*/
public function ensureValidToken(): void
{
if ($this->oauthAccount === null) {
return;
}
$newToken = $this->tokenManager->ensureValidToken($this->oauthAccount);
if ($newToken !== null) {
$this->accessToken = $newToken;
}
}
public function getConfig()
{
return $this->config;
}
// returns only active (archived=false)
public function getOwners(): array
{
return $this->getNewInstance()->crm()->owners()->getAll();
}
/**
* @param bool $archived
*
* @return array<Owner>|[]
*/
public function getOwnersArchived(bool $archived = true): array
{
$endpoint = '/crm/v3/owners';
$queryParams = [
'archived' => $archived ? 'true' : 'false',
];
$queryString = http_build_query($queryParams);
$owners = [];
try {
$response = $this->makeRequest(endpoint: $endpoint, queryString: $queryString);
$responseData = $response?->toArray();
foreach ($responseData['results'] as $result) {
try {
$owners[] = Owner::create($result);
} catch (Throwable $e) {
$this->log->error('[HubSpot] Failed to process owner data', [
'result' => $result,
'error' => $e->getMessage(),
]);
continue;
}
}
} catch (Throwable $e) {
$this->log->error('HubSpot] Failed to fetch owners', [
'archived' => $archived,
'error' => $e->getMessage(),
]);
return [];
}
return $owners;
}
public function getMeeting(string $engagementId): ObjectWithAssociations
{
return $this->getNewInstance()->crm()->objects()->basicApi()
->getById('meeting', $engagementId, null, 'contact,company,deal');
}
public function deleteEngagement(string $engagementId): void
{
$this->getInstance()->engagements()->delete((int) $engagementId);
}
public function getAssociationsData(array $ids, string $fromObject, string $toObject): array
{
$associationData = [];
$idChunks = array_chunk($ids, self::ASSOCIATIONS_BATCH_SIZE_LIMIT);
foreach ($idChunks as $idChunk) {
try {
$batchInput = new \HubSpot\Client\Crm\Associations\Model\BatchInputPublicObjectId();
$batchInput->setInputs(array_map(function ($id) {
$publicObjectId = new \HubSpot\Client\Crm\Associations\Model\PublicObjectId();
$publicObjectId->setId($id);
return $publicObjectId;
}, $idChunk));
$associatedObjectsData = $this
->getNewInstance()
->crm()
->associations()
->batchApi()
->read($fromObject, $toObject, $batchInput);
if ($associatedObjectsData instanceof \HubSpot\Client\Crm\Associations\Model\BatchResponsePublicAssociationMulti) {
foreach ($associatedObjectsData->getResults() as $association) {
$from = $association->getFrom()->getId();
$toAssociations = $association->getTo();
if (! empty($toAssociations)) {
$associationData[$from] = array_map(function ($item) {
return $item->getId();
}, $toAssociations);
}
}
}
} catch (\Exception $e) {
$this->log->error('[Hubspot] Failed to fetch associations', [
'from_object' => $fromObject,
'to_object' => $toObject,
'reason' => $e->getMessage(),
]);
}
}
return $associationData;
}
/**
* @throws \Exception
*/
private function getNoteAssociationType(NoteObject $noteObject): string
{
return match($noteObject) {
NoteObject::Opportunity => 'note_to_deal',
NoteObject::Lead, NoteObject::Contact => 'note_to_contact', // or 'note_to_lead' if your portal supports it
NoteObject::Account => 'note_to_company',
NoteObject::Call, NoteObject::Event => throw new \Exception('Not supported'),
};
}
/**
* @throws \Exception
*/
private function getNoteObject(NoteObject $noteObject): string
{
return match($noteObject) {
NoteObject::Opportunity => 'deal',
NoteObject::Lead, NoteObject::Contact => 'contact',
NoteObject::Account => 'company',
NoteObject::Call, NoteObject::Event => throw new \Exception('Not supported'),
};
}
public function addAssociations(string $objectType, string $associationType, array $payload): Response
{
$endpoint = "/crm/v4/associations/$objectType/$associationType/batch/create";
return $this->makeRequest($endpoint, 'POST', $payload);
}
public function removeAssociations(string $objectType, string $associationType, array $payload): Response
{
$endpoint = "/crm/v4/associations/$objectType/$associationType/batch/archive";
return $this->makeRequest($endpoint, 'POST', $payload);
}
}
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# [PASSWORD_DOTS] HS [PASSWORD_DOTS]
select * from teams where id = 2; # 2
select * from features; # 2
select * from team_features where team_id = 2; # 2
select * from crm_configurations where id = 2; # 2
select * from users where team_id = 2; #
select * from playbooks where team_id = 2; # event 38
select * from playbook_categories where playbook_id = 38; #
SELECT * FROM activities WHERE crm_configuration_id = 2 and crm_provider_id is not null order by id desc;
[URL_WITH_CREDENTIALS] CrmFieldOption array{id:string, label:string, value?:string}
*/
class Client extends BaseClient implements HubspotClientInterface
{
public const string MIN_API_VERSION = '2';
public const string BASE_URL = '[URL_WITH_CREDENTIALS] T
* @param callable(): T $apiCall
* @return T
*
* @throws RateLimitException
*/
private function executeRequest(callable $apiCall)
{
if (! $this->rateLimiter->canMakeRequest($this->config)) {
$retryAfter = $this->rateLimiter->requestAvailableIn($this->config);
$this->log->warning('[Hubspot] Rate limit exceeded, deferring request', [
'team_id' => $this->config->team_id,
'config_id' => $this->config->getId(),
'retry_after' => $retryAfter,
]);
throw new RateLimitException(
'Hubspot rate limit reached for configuration ' . $this->config->getId(),
$retryAfter,
);
}
$this->rateLimiter->incrementRequestCount($this->config);
try {
return $apiCall();
} catch (Throwable $e) {
if ($this->isHubspotRateLimit($e)) {
$retryAfter = $this->parseRetryAfter($e);
$this->log->warning('[Hubspot] Received 429 from API', [
'team_id' => $this->config->team_id,
'config_id' => $this->config->getId(),
'retry_after' => $retryAfter,
'reason' => $e->getMessage(),
]);
throw new RateLimitException('Hubspot returned 429', $retryAfter, $e);
}
throw $e;
}
}
private function isHubspotRateLimit(Throwable $e): bool
{
return method_exists($e, 'getCode') && (int) $e->getCode() === 429;
}
private function parseRetryAfter(Throwable $e): int
{
if (method_exists($e, 'getResponseHeaders')) {
$headers = $e->getResponseHeaders() ?: [];
$value = $headers['Retry-After'] ?? $headers['retry-after'] ?? null;
if (is_array($value)) {
$value = $value[0] ?? null;
}
if (is_numeric($value)) {
return (int) $value;
}
}
return 10;
}
public function getMinimumApiVersion(): string
{
return self::MIN_API_VERSION;
}
public function getInstance(): Factory
{
return new Factory([
'key' => $this->accessToken,
'oauth2' => true,
'base_url' => $this->baseUrl,
]);
}
public function getNewInstance(): Discovery
{
return \HubSpot\Factory::createWithAccessToken($this->accessToken);
}
/**
* Secondly and daily limits for Hubspot API
*
* Product Tier: Free & Starter | Professional & Enterprise | API add-on (any tier)
* Burst: 100/10 seconds | 150/10 seconds | 200/10 seconds
* Daily: 250,000 | 500,000 | 1,000,000
*
* Official documentation states: The search endpoints are rate limited to five requests per second.
* Since with 5 RPS were still hitting secondly rate limits we lowered it to 4
*/
public function getPaginatedData(array $payload, string $type, int $offset = 0): array
{
$total = 0;
$lastId = null;
$rows = [];
foreach ($this->getPaginatedDataGenerator($payload, $type, $offset, $total, $lastId) as $row) {
$rows[] = $row;
}
return ['results' => $rows, 'total' => $total, 'last_record' => $lastId];
}
/**
* @throws HubspotException
* @throws SocialAccountTokenInvalidException
* @throws BadRequest
*/
public function getPaginatedDataGenerator(
array $payload,
string $type,
int $offset = 0,
int &$total = 0,
?string &$lastRecordId = null
): \Generator {
return $this->paginationService->getPaginatedDataGenerator(
$this,
$payload,
$type,
$offset,
$total,
$lastRecordId
);
}
/**
* @throws DealApiException
* @throws CrmException
*/
public function getOpportunityById(string $crmId, array $fields): array
{
try {
$deal = $this->executeRequest(fn () => $this->getNewInstance()->crm()->deals()->basicApi()->getById(
$crmId,
implode(',', $fields),
'companies,contacts'
));
} catch (DealApiException $e) {
$this->log->info('[Hubspot] Failed to fetch opportunity', [
'crm_id' => $crmId,
'reason' => $e->getMessage(),
]);
throw $e;
}
if (! $deal instanceof DealWithAssociations) {
throw new CrmException('Deal not found');
}
return [
'id' => $deal->getId(),
'properties' => $deal->getProperties(),
'associations' => $deal->getAssociations(),
];
}
/**
* Generic batch read method for HubSpot objects
*
* @param string $objectType The object type ('deals', 'companies', 'contacts')
* @param array<string> $crmIds Array of HubSpot object IDs (max 100)
* @param array<string> $fields Array of property names to fetch
*
* @return array<string, array> Array keyed by CRM ID with object data
*/
private function batchReadObjects(string $objectType, array $crmIds, array $fields): array
{
if (empty($crmIds)) {
return [];
}
$this->validateBatchSize($objectType, $crmIds);
$this->ensureValidToken();
try {
$batchConfig = $this->createBatchConfiguration($objectType);
$batchReadRequest = $this->prepareBatchRequest($batchConfig, $crmIds, $fields);
$response = $batchConfig['api']->read($batchReadRequest);
$this->validateApiResponse($response, $objectType);
$results = $this->processApiResults($response);
$this->logBatchResults($objectType, $crmIds, $results);
return $results;
} catch (\Throwable $e) {
$this->handleBatchError($e, $objectType, $crmIds);
}
}
private function validateBatchSize(string $objectType, array $crmIds): void
{
if (count($crmIds) > 100) {
throw new \InvalidArgumentException("Batch size cannot exceed 100 {$objectType}");
}
}
private function createBatchConfiguration(string $objectType): array
{
$configurations = [
'deals' => [
'batchReadRequest' => new \HubSpot\Client\Crm\Deals\Model\BatchReadInputSimplePublicObjectId(),
'inputClass' => \HubSpot\Client\Crm\Deals\Model\SimplePublicObjectId::class,
'api' => $this->getNewInstance()->crm()->deals()->batchApi(),
],
'companies' => [
'batchReadRequest' => new \HubSpot\Client\Crm\Companies\Model\BatchReadInputSimplePublicObjectId(),
'inputClass' => \HubSpot\Client\Crm\Companies\Model\SimplePublicObjectId::class,
'api' => $this->getNewInstance()->crm()->companies()->batchApi(),
],
'contacts' => [
'batchReadRequest' => new \HubSpot\Client\Crm\Contacts\Model\BatchReadInputSimplePublicObjectId(),
'inputClass' => \HubSpot\Client\Crm\Contacts\Model\SimplePublicObjectId::class,
'api' => $this->getNewInstance()->crm()->contacts()->batchApi(),
],
];
if (! isset($configurations[$objectType])) {
throw new \InvalidArgumentException("Unsupported object type: {$objectType}");
}
return $configurations[$objectType];
}
private function prepareBatchRequest(array $batchConfig, array $crmIds, array $fields): object
{
$batchReadRequest = $batchConfig['batchReadRequest'];
$inputClass = $batchConfig['inputClass'];
$inputs = array_map(function ($crmId) use ($inputClass) {
$input = new $inputClass();
$input->setId($crmId);
return $input;
}, $crmIds);
$batchReadRequest->setInputs($inputs);
$batchReadRequest->setProperties($fields);
return $batchReadRequest;
}
private function validateApiResponse($response, string $objectType): void
{
if (! $response) {
throw new CrmException("HubSpot API returned null response for {$objectType} batch read");
}
}
private function processApiResults($response): array
{
$results = [];
$responseResults = $response->getResults();
if ($responseResults) {
foreach ($responseResults as $object) {
if ($object && $object->getId()) {
$results[$object->getId()] = [
'id' => $object->getId(),
'properties' => $object->getProperties() ?: [],
];
}
}
}
return $results;
}
private function logBatchResults(string $objectType, array $crmIds, array $results): void
{
$this->log->info("[HubSpot] Batch fetched {$objectType}", [
'requested_count' => count($crmIds),
'returned_count' => count($results),
'crm_ids' => $crmIds,
]);
}
private function handleBatchError(\Throwable $e, string $objectType, array $crmIds): void
{
$errorMessage = $e->getMessage() ?: 'Unknown error';
$errorTrace = $e->getTraceAsString() ?: 'No trace available';
$this->log->error("[HubSpot] Failed to batch fetch {$objectType}", [
'crm_ids' => $crmIds,
'error' => $errorMessage,
'trace' => $errorTrace,
]);
throw new CrmException("Failed to batch fetch {$objectType}: " . $errorMessage);
}
/**
* Batch read multiple opportunities by their CRM IDs
*
* @param array<string> $crmIds Array of HubSpot deal IDs (max 100)
* @param array<string> $fields Array of property names to fetch
*
* @return array<string, array> Array keyed by CRM ID with opportunity data
*/
public function getOpportunitiesByIds(array $crmIds, array $fields): array
{
return $this->batchReadObjects('deals', $crmIds, $fields);
}
/**
* Batch read multiple companies by their CRM IDs
*
* @param array<string> $crmIds Array of HubSpot company IDs (max 100)
* @param array<string> $fields Array of property names to fetch
*
* @return array<string, array> Array keyed by CRM ID with company data
*/
public function getCompaniesByIds(array $crmIds, array $fields): array
{
return $this->batchReadObjects('companies', $crmIds, $fields);
}
/**
* Batch read multiple contacts by their CRM IDs
*
* @param array<string> $crmIds Array of HubSpot contact IDs (max 100)
* @param array<string> $fields Array of property names to fetch
*
* @return array<string, array> Array keyed by CRM ID with contact data
*/
public function getContactsByIds(array $crmIds, array $fields): array
{
return $this->batchReadObjects('contacts', $crmIds, $fields);
}
/**
* @throws CompanyApiException
* @throws CrmException
*/
public function getAccountById(string $crmId, array $fields): array
{
try {
$company = $this->getNewInstance()->crm()->companies()->basicApi()->getById(
$crmId,
implode(',', $fields),
);
} catch (CompanyApiException $e) {
$this->log->info('[Hubspot] Failed to fetch account', [
'crm_id' => $crmId,
'reason' => $e->getMessage(),
]);
throw $e;
}
if (! $company instanceof CompaniesWithAssociations) {
throw new CrmException('Account not found');
}
return [
'id' => $company->getId(),
'properties' => $company->getProperties(),
];
}
/**
* @throws ContactApiException
* @throws CrmException
*/
public function getContactById(string $crmId, array $fields): array
{
try {
$contact = $this->getNewInstance()->crm()->contacts()->basicApi()->getById(
$crmId,
implode(',', $fields)
);
} catch (ContactApiException $e) {
$this->log->info('[Hubspot] Failed to fetch contact', [
'crm_id' => $crmId,
'reason' => $e->getMessage(),
]);
throw $e;
}
if (! $contact instanceof ContactsWithAssociations) {
throw new CrmException('Contact not found');
}
return [
'id' => $contact->getId(),
'properties' => $contact->getProperties(),
];
}
/**
* This is email search request that Hubspot offers as GET (more generous quota)
*/
public function getContactByEmail(string $email, array $fields = []): array
{
try {
$contact = $this->getNewInstance()->crm()->contacts()->basicApi()->getById(
$email,
implode(',', $fields),
null,
false,
'email'
);
return [
'id' => $contact->getId(),
'properties' => $contact->getProperties(),
];
} catch (ContactApiException $e) {
$this->log->info('[Hubspot] Failed to fetch contact', [
'email' => $email,
'reason' => $e->getMessage(),
]);
return [];
}
}
/**
* @throws CrmException
*/
public function fetchProperty(string $objectType, string $propertyId): Property
{
$result = $this->getNewInstance()->crm()->properties()->coreApi()->getByName($objectType, $propertyId);
if (! $result instanceof Property) {
$this->log->error('[Hubspot] Failed to fetch property', [
'object_type' => $objectType,
'property_id' => $propertyId,
'reason' => $result->getMessage(),
]);
throw new CrmException('Failed to fetch property');
}
return $result;
}
/**
* @return array<CrmFieldOption>
*/
public function fetchPropertyOptions(string $objectType, string $propertyId): array
{
/** @var array<CrmFieldOption> */
return $this->fetchProperty($objectType, $propertyId)->getOptions();
}
/**
* @return array<array{id:string, label:string, deleted:bool}>
*/
public function fetchCallDispositions(): array
{
/** @var Response $response */
$response = $this->getInstance()->engagements()->getCallDispositions();
/**
* @var array<array{
* id:string,
* label:string,
* deleted: bool
* }>
*/
return $response->toArray();
}
/**
* @return array<CrmFieldOption>
*/
public function fetchOpportunityPipelineStages(): array
{
$stages = [];
$apiResponse = $this->getNewInstance()->crm()->pipelines()->pipelinesApi()->getAll('deals');
if ($apiResponse instanceof Error) {
$this->log->error('[Hubspot] Failed to fetch opportunity pipelines', [
'reason' => $apiResponse->getMessage(),
]);
return [];
}
foreach ($apiResponse->getResults() as $pipeline) {
$pipelineStages = array_map(
static function (PipelineStage $stage) {
return [
'id' => $stage->getId(),
'label' => $stage->getLabel(),
];
},
$pipeline->getStages()
);
$stages = array_merge($stages, $pipelineStages);
}
return $stages;
}
public function fetchOpportunityPipelines(): array
{
$pipelines = [];
try {
$apiResponse = $this->makeRequest('/crm/v3/pipelines/deals');
} catch (\Exception $e) {
$this->log->info('[Hubspot] Failed to fetch opportunity pipelines', [
'reason' => $e->getMessage(),
]);
return [];
}
$response = $apiResponse->toArray();
foreach ($response['results'] as $pipeline) {
$pipelines[] = [
'id' => $pipeline['id'],
'label' => $pipeline['label'],
];
}
return $pipelines;
}
/**
* @return array<CrmFieldOption>
*/
public function fetchMeetingOutcomeFieldOptions(Field $field): array
{
return $field->getCrmProviderId() === 'meetingOutcome'
? $this->fetchMeetingOutcomeTypes()
: $this->fetchCallActivityTypes();
}
public function fetchMeetingOutcomeTypes(): array
{
return $this->extractMeetingTypeOptions(
'[URL_WITH_CREDENTIALS] Response $response */
$response = $this->getInstance()
->getClient()
->request('GET', $endpoint);
/**
* @var array<array{
* value: string,
* label: string,
* displayOrder: int
* }> $optionData
*/
$optionData = $response->toArray()['options'] ?? [];
$options = [];
foreach ($optionData as $item) {
$options[] = [
'id' => $item['value'],
'value' => $item['value'],
'label' => $item['label'],
'display_order' => $item['displayOrder'],
];
}
return $options;
}
/**
* @return array<CrmFieldOption>
*/
public function fetchDispositionFieldOptions(): array
{
$options = [];
$dispositions = $this->fetchCallDispositions();
foreach ($dispositions as $disposition) {
if ($disposition['deleted'] !== false) {
continue;
}
$option['value'] = $disposition['id'];
$option['id'] = $disposition['id'];
$option['label'] = $disposition['label'];
$options[] = $option;
}
return $options;
}
/**
* @return array<CrmFieldOption>
*/
public function fetchOpportunityFieldOptions(Field $field): array
{
if ($field->isStageField()) {
return $this->fetchOpportunityPipelineStages();
}
if ($field->isPipelineField()) {
return $this->fetchOpportunityPipelines();
}
return $this->fetchPropertyOptions('deals', $field->getCrmProviderId());
}
/**
* @throws BadRequest
* @throws HubspotException
*/
public function makeRequest(string $endpoint, $method = 'GET', $payload = [], ?string $queryString = null)
{
$endpoint = self::BASE_URL . $endpoint;
if ($method === 'GET') {
$response = $this->getInstance()->getClient()?->request(
method: $method,
endpoint: $endpoint,
query_string: $queryString
);
} else {
$response = $this->getInstance()->getClient()->request($method, $endpoint, [
'json' => ($payload),
]);
}
return $response;
}
/**
* @throws BadRequest
* @throws HubspotException
*/
public function createMeeting(array $payload): Response
{
$endpoint = '/crm/v3/objects/meetings';
return $this->makeRequest($endpoint, 'POST', $payload);
}
/**
* @throws BadRequest
* @throws HubspotException
*/
public function updateMeeting(string $meetingId, array $payload): Response
{
$endpoint = '/crm/v3/objects/meetings/' . $meetingId;
return $this->makeRequest($endpoint, 'PATCH', $payload);
}
/**
* @throws \Exception
*/
public function createNote(
string $body,
string $ownerId,
int $timestamp,
string $objectId,
NoteObject $noteObject
): ?string {
try {
$noteInput = new SimplePublicObjectInput([
'properties' => [
'hs_note_body' => $body,
'hubspot_owner_id' => $ownerId,
'hs_timestamp' => $timestamp,
],
]);
// Create note
$note = $this->getNewInstance()->crm()->objects()->basicApi()->create('note', $noteInput);
$this->getNewInstance()->crm()->objects()->associationsApi()->create(
'note',
$note->getId(),
$this->getNoteObject($noteObject),
$objectId,
$this->getNoteAssociationType($noteObject),
);
return $note->getId();
} catch (\Exception $e) {
$this->log->error('[Hubspot] Failed to create note', [
'objectId' => $objectId,
'noteObject' => $noteObject->getObjectType(),
'reason' => $e->getMessage(),
]);
\Sentry::captureException($e);
}
return null;
}
public function updateEngagement(string $objectId, array $engagement, array $metadata): void
{
$this->getInstance()->engagements()->update($objectId, $engagement, $metadata);
}
public function getEngagementData(string $engagementId): array
{
$engagement = $this->getInstance()->engagements()->get($engagementId);
return $engagement->toArray();
}
public function createEngagement(array $engagement, array $associations, array $metadata): Response
{
return $this->getInstance()
->engagements()
->create($engagement, $associations, $metadata);
}
public function isUnauthorizedException(\Exception $e): bool
{
// Check for specific HubSpot API exception types first
if ($e instanceof BadRequest) {
// BadRequest can contain 401 status codes
return $e->getCode() === 401;
}
// Check for HTTP client exceptions with status codes
if ($e instanceof \GuzzleHttp\Exception\RequestException && $e->hasResponse()) {
$response = $e->getResponse();
if ($response !== null) {
return $response->getStatusCode() === 401;
}
}
// Check for Guzzle HTTP exceptions
if ($e instanceof \GuzzleHttp\Exception\ClientException) {
return $e->getCode() === 401;
}
// Fallback to string matching as last resort, but be more specific
$message = strtolower($e->getMessage());
return str_contains($message, '401 unauthorized') ||
str_contains($message, 'http 401') ||
str_contains($message, 'status code 401') ||
(preg_match('/\b401\b/', $message) && str_contains($message, 'unauthorized'));
}
/**
* Validates and refreshes the access token if needed before API requests.
* This ensures long-running processes don't fail due to token expiration.
*
* @throws SocialAccountTokenInvalidException
*/
public function ensureValidToken(): void
{
if ($this->oauthAccount === null) {
return;
}
$newToken = $this->tokenManager->ensureValidToken($this->oauthAccount);
if ($newToken !== null) {
$this->accessToken = $newToken;
}
}
public function getConfig()
{
return $this->config;
}
// returns only active (archived=false)
public function getOwners(): array
{
return $this->getNewInstance()->crm()->owners()->getAll();
}
/**
* @param bool $archived
*
* @return array<Owner>|[]
*/
public function getOwnersArchived(bool $archived = true): array
{
$endpoint = '/crm/v3/owners';
$queryParams = [
'archived' => $archived ? 'true' : 'false',
];
$queryString = http_build_query($queryParams);
$owners = [];
try {
$response = $this->makeRequest(endpoint: $endpoint, queryString: $queryString);
$responseData = $response?->toArray();
foreach ($responseData['results'] as $result) {
try {
$owners[] = Owner::create($result);
} catch (Throwable $e) {
$this->log->error('[HubSpot] Failed to process owner data', [
'result' => $result,
'error' => $e->getMessage(),
]);
continue;
}
}
} catch (Throwable $e) {
$this->log->error('HubSpot] Failed to fetch owners', [
'archived' => $archived,
'error' => $e->getMessage(),
]);
return [];
}
return $owners;
}
public function getMeeting(string $engagementId): ObjectWithAssociations
{
return $this->getNewInstance()->crm()->objects()->basicApi()
->getById('meeting', $engagementId, null, 'contact,company,deal');
}
public function deleteEngagement(string $engagementId): void
{
$this->getInstance()->engagements()->delete((int) $engagementId);
}
public function getAssociationsData(array $ids, string $fromObject, string $toObject): array
{
$associationData = [];
$idChunks = array_chunk($ids, self::ASSOCIATIONS_BATCH_SIZE_LIMIT);
foreach ($idChunks as $idChunk) {
try {
$batchInput = new \HubSpot\Client\Crm\Associations\Model\BatchInputPublicObjectId();
$batchInput->setInputs(array_map(function ($id) {
$publicObjectId = new \HubSpot\Client\Crm\Associations\Model\PublicObjectId();
$publicObjectId->setId($id);
return $publicObjectId;
}, $idChunk));
$associatedObjectsData = $this
->getNewInstance()
->crm()
->associations()
->batchApi()
->read($fromObject, $toObject, $batchInput);
if ($associatedObjectsData instanceof \HubSpot\Client\Crm\Associations\Model\BatchResponsePublicAssociationMulti) {
foreach ($associatedObjectsData->getResults() as $association) {
$from = $association->getFrom()->getId();
$toAssociations = $association->getTo();
if (! empty($toAssociations)) {
$associationData[$from] = array_map(function ($item) {
return $item->getId();
}, $toAssociations);
}
}
}
} catch (\Exception $e) {
$this->log->error('[Hubspot] Failed to fetch associations', [
'from_object' => $fromObject,
'to_object' => $toObject,
'reason' => $e->getMessage(),
]);
}
}
return $associationData;
}
/**
* @throws \Exception
*/
private function getNoteAssociationType(NoteObject $noteObject): string
{
return match($noteObject) {
NoteObject::Opportunity => 'note_to_deal',
NoteObject::Lead, NoteObject::Contact => 'note_to_contact', // or 'note_to_lead' if your portal supports it
NoteObject::Account => 'note_to_company',
NoteObject::Call, NoteObject::Event => throw new \Exception('Not supported'),
};
}
/**
* @throws \Exception
*/
private function getNoteObject(NoteObject $noteObject): string
{
return match($noteObject) {
NoteObject::Opportunity => 'deal',
NoteObject::Lead, NoteObject::Contact => 'contact',
NoteObject::Account => 'company',
NoteObject::Call, NoteObject::Event => throw new \Exception('Not supported'),
};
}
public function addAssociations(string $objectType, string $associationType, array $payload): Response
{
$endpoint = "/crm/v4/associations/$objectType/$associationType/batch/create";
return $this->makeRequest($endpoint, 'POST', $payload);
}
public function removeAssociations(string $objectType, string $associationType, array $payload): Response
{
$endpoint = "/crm/v4/associations/$objectType/$associationType/batch/archive";
return $this->makeRequest($endpoint, 'POST', $payload);
}
}
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# [PASSWORD_DOTS] HS [PASSWORD_DOTS]
select * from teams where id = 2; # 2
select * from features; # 2
select * from team_features where team_id = 2; # 2
select * from crm_configurations where id = 2; # 2
select * from users where team_id = 2; #
select * from playbooks where team_id = 2; # event 38
select * from playbook_categories where playbook_id = 38; #
SELECT * FROM activities WHERE crm_configuration_id = 2 and crm_provider_id is not null order by id desc;
[URL_WITH_CREDENTIALS] CrmFieldOption array{id:string, label:string, value?:string}
*/
class Client extends BaseClient implements HubspotClientInterface
{
public const string MIN_API_VERSION = '2';
public const string BASE_URL = '[URL_WITH_CREDENTIALS] T
* @param callable(): T $apiCall
* @return T
*
* @throws RateLimitException
*/
private function executeRequest(callable $apiCall)
{
if (! $this->rateLimiter->canMakeRequest($this->config)) {
$retryAfter = $this->rateLimiter->requestAvailableIn($this->config);
$this->log->warning('[Hubspot] Rate limit exceeded, deferring request', [
'team_id' => $this->config->team_id,
'config_id' => $this->config->getId(),
'retry_after' => $retryAfter,
]);
throw new RateLimitException(
'Hubspot rate limit reached for configuration ' . $this->config->getId(),
$retryAfter,
);
}
$this->rateLimiter->incrementRequestCount($this->config);
try {
return $apiCall();
} catch (Throwable $e) {
if ($this->isHubspotRateLimit($e)) {
$retryAfter = $this->parseRetryAfter($e);
$this->log->warning('[Hubspot] Received 429 from API', [
'team_id' => $this->config->team_id,
'config_id' => $this->config->getId(),
'retry_after' => $retryAfter,
'reason' => $e->getMessage(),
]);
throw new RateLimitException('Hubspot returned 429', $retryAfter, $e);
}
throw $e;
}
}
private function isHubspotRateLimit(Throwable $e): bool
{
return method_exists($e, 'getCode') && (int) $e->getCode() === 429;
}
private function parseRetryAfter(Throwable $e): int
{
if (method_exists($e, 'getResponseHeaders')) {
$headers = $e->getResponseHeaders() ?: [];
$value = $headers['Retry-After'] ?? $headers['retry-after'] ?? null;
if (is_array($value)) {
$value = $value[0] ?? null;
}
if (is_numeric($value)) {
return (int) $value;
}
}
return 10;
}
public function getMinimumApiVersion(): string
{
return self::MIN_API_VERSION;
}
public function getInstance(): Factory
{
return new Factory([
'key' => $this->accessToken,
'oauth2' => true,
'base_url' => $this->baseUrl,
]);
}
public function getNewInstance(): Discovery
{
return \HubSpot\Factory::createWithAccessToken($this->accessToken);
}
/**
* Secondly and daily limits for Hubspot API
*
* Product Tier: Free & Starter | Professional & Enterprise | API add-on (any tier)
* Burst: 100/10 seconds | 150/10 seconds | 200/10 seconds
* Daily: 250,000 | 500,000 | 1,000,000
*
* Official documentation states: The search endpoints are rate limited to five requests per second.
* Since with 5 RPS were still hitting secondly rate limits we lowered it to 4
*/
public function getPaginatedData(array $payload, string $type, int $offset = 0): array
{
$total = 0;
$lastId = null;
$rows = [];
foreach ($this->getPaginatedDataGenerator($payload, $type, $offset, $total, $lastId) as $row) {
$rows[] = $row;
}
return ['results' => $rows, 'total' => $total, 'last_record' => $lastId];
}
/**
* @throws HubspotException
* @throws SocialAccountTokenInvalidException
* @throws BadRequest
*/
public function getPaginatedDataGenerator(
array $payload,
string $type,
int $offset = 0,
int &$total = 0,
?string &$lastRecordId = null
): \Generator {
return $this->paginationService->getPaginatedDataGenerator(
$this,
$payload,
$type,
$offset,
$total,
$lastRecordId
);
}
/**
* @throws DealApiException
* @throws CrmException
*/
public function getOpportunityById(string $crmId, array $fields): array
{
try {
$deal = $this->executeRequest(fn () => $this->getNewInstance()->crm()->deals()->basicApi()->getById(
$crmId,
implode(',', $fields),
'companies,contacts'
));
} catch (DealApiException $e) {
$this->log->info('[Hubspot] Failed to fetch opportunity', [
'crm_id' => $crmId,
'reason' => $e->getMessage(),
]);
throw $e;
}
if (! $deal instanceof DealWithAssociations) {
throw new CrmException('Deal not found');
}
return [
'id' => $deal->getId(),
'properties' => $deal->getProperties(),
'associations' => $deal->getAssociations(),
];
}
/**
* Generic batch read method for HubSpot objects
*
* @param string $objectType The object type ('deals', 'companies', 'contacts')
* @param array<string> $crmIds Array of HubSpot object IDs (max 100)
* @param array<string> $fields Array of property names to fetch
*
* @return array<string, array> Array keyed by CRM ID with object data
*/
private function batchReadObjects(string $objectType, array $crmIds, array $fields): array
{
if (empty($crmIds)) {
return [];
}
$this->validateBatchSize($objectType, $crmIds);
$this->ensureValidToken();
try {
$batchConfig = $this->createBatchConfiguration($objectType);
$batchReadRequest = $this->prepareBatchRequest($batchConfig, $crmIds, $fields);
$response = $batchConfig['api']->read($batchReadRequest);
$this->validateApiResponse($response, $objectType);
$results = $this->processApiResults($response);
$this->logBatchResults($objectType, $crmIds, $results);
return $results;
} catch (\Throwable $e) {
$this->handleBatchError($e, $objectType, $crmIds);
}
}
private function validateBatchSize(string $objectType, array $crmIds): void
{
if (count($crmIds) > 100) {
throw new \InvalidArgumentException("Batch size cannot exceed 100 {$objectType}");
}
}
private function createBatchConfiguration(string $objectType): array
{
$configurations = [
'deals' => [
'batchReadRequest' => new \HubSpot\Client\Crm\Deals\Model\BatchReadInputSimplePublicObjectId(),
'inputClass' => \HubSpot\Client\Crm\Deals\Model\SimplePublicObjectId::class,
'api' => $this->getNewInstance()->crm()->deals()->batchApi(),
],
'companies' => [
'batchReadRequest' => new \HubSpot\Client\Crm\Companies\Model\BatchReadInputSimplePublicObjectId(),
'inputClass' => \HubSpot\Client\Crm\Companies\Model\SimplePublicObjectId::class,
'api' => $this->getNewInstance()->crm()->companies()->batchApi(),
],
'contacts' => [
'batchReadRequest' => new \HubSpot\Client\Crm\Contacts\Model\BatchReadInputSimplePublicObjectId(),
'inputClass' => \HubSpot\Client\Crm\Contacts\Model\SimplePublicObjectId::class,
'api' => $this->getNewInstance()->crm()->contacts()->batchApi(),
],
];
if (! isset($configurations[$objectType])) {
throw new \InvalidArgumentException("Unsupported object type: {$objectType}");
}
return $configurations[$objectType];
}
private function prepareBatchRequest(array $batchConfig, array $crmIds, array $fields): object
{
$batchReadRequest = $batchConfig['batchReadRequest'];
$inputClass = $batchConfig['inputClass'];
$inputs = array_map(function ($crmId) use ($inputClass) {
$input = new $inputClass();
$input->setId($crmId);
return $input;
}, $crmIds);
$batchReadRequest->setInputs($inputs);
$batchReadRequest->setProperties($fields);
return $batchReadRequest;
}
private function validateApiResponse($response, string $objectType): void
{
if (! $response) {
throw new CrmException("HubSpot API returned null response for {$objectType} batch read");
}
}
private function processApiResults($response): array
{
$results = [];
$responseResults = $response->getResults();
if ($responseResults) {
foreach ($responseResults as $object) {
if ($object && $object->getId()) {
$results[$object->getId()] = [
'id' => $object->getId(),
'properties' => $object->getProperties() ?: [],
];
}
}
}
return $results;
}
private function logBatchResults(string $objectType, array $crmIds, array $results): void
{
$this->log->info("[HubSpot] Batch fetched {$objectType}", [
'requested_count' => count($crmIds),
'returned_count' => count($results),
'crm_ids' => $crmIds,
]);
}
private function handleBatchError(\Throwable $e, string $objectType, array $crmIds): void
{
$errorMessage = $e->getMessage() ?: 'Unknown error';
$errorTrace = $e->getTraceAsString() ?: 'No trace available';
$this->log->error("[HubSpot] Failed to batch fetch {$objectType}", [
'crm_ids' => $crmIds,
'error' => $errorMessage,
'trace' => $errorTrace,
]);
throw new CrmException("Failed to batch fetch {$objectType}: " . $errorMessage);
}
/**
* Batch read multiple opportunities by their CRM IDs
*
* @param array<string> $crmIds Array of HubSpot deal IDs (max 100)
* @param array<string> $fields Array of property names to fetch
*
* @return array<string, array> Array keyed by CRM ID with opportunity data
*/
public function getOpportunitiesByIds(array $crmIds, array $fields): array
{
return $this->batchReadObjects('deals', $crmIds, $fields);
}
/**
* Batch read multiple companies by their CRM IDs
*
* @param array<string> $crmIds Array of HubSpot company IDs (max 100)
* @param array<string> $fields Array of property names to fetch
*
* @return array<string, array> Array keyed by CRM ID with company data
*/
public function getCompaniesByIds(array $crmIds, array $fields): array
{
return $this->batchReadObjects('companies', $crmIds, $fields);
}
/**
* Batch read multiple contacts by their CRM IDs
*
* @param array<string> $crmIds Array of HubSpot contact IDs (max 100)
* @param array<string> $fields Array of property names to fetch
*
* @return array<string, array> Array keyed by CRM ID with contact data
*/
public function getContactsByIds(array $crmIds, array $fields): array
{
return $this->batchReadObjects('contacts', $crmIds, $fields);
}
/**
* @throws CompanyApiException
* @throws CrmException
*/
public function getAccountById(string $crmId, array $fields): array
{
try {
$company = $this->getNewInstance()->crm()->companies()->basicApi()->getById(
$crmId,
implode(',', $fields),
);
} catch (CompanyApiException $e) {
$this->log->info('[Hubspot] Failed to fetch account', [
'crm_id' => $crmId,
'reason' => $e->getMessage(),
]);
throw $e;
}
if (! $company instanceof CompaniesWithAssociations) {
throw new CrmException('Account not found');
}
return [
'id' => $company->getId(),
'properties' => $company->getProperties(),
];
}
/**
* @throws ContactApiException
* @throws CrmException
*/
public function getContactById(string $crmId, array $fields): array
{
try {
$contact = $this->getNewInstance()->crm()->contacts()->basicApi()->getById(
$crmId,
implode(',', $fields)
);
} catch (ContactApiException $e) {
$this->log->info('[Hubspot] Failed to fetch contact', [
'crm_id' => $crmId,
'reason' => $e->getMessage(),
]);
throw $e;
}
if (! $contact instanceof ContactsWithAssociations) {
throw new CrmException('Contact not found');
}
return [
'id' => $contact->getId(),
'properties' => $contact->getProperties(),
];
}
/**
* This is email search request that Hubspot offers as GET (more generous quota)
*/
public function getContactByEmail(string $email, array $fields = []): array
{
try {
$contact = $this->getNewInstance()->crm()->contacts()->basicApi()->getById(
$email,
implode(',', $fields),
null,
false,
'email'
);
return [
'id' => $contact->getId(),
'properties' => $contact->getProperties(),
];
} catch (ContactApiException $e) {
$this->log->info('[Hubspot] Failed to fetch contact', [
'email' => $email,
'reason' => $e->getMessage(),
]);
return [];
}
}
/**
* @throws CrmException
*/
public function fetchProperty(string $objectType, string $propertyId): Property
{
$result = $this->getNewInstance()->crm()->properties()->coreApi()->getByName($objectType, $propertyId);
if (! $result instanceof Property) {
$this->log->error('[Hubspot] Failed to fetch property', [
'object_type' => $objectType,
'property_id' => $propertyId,
'reason' => $result->getMessage(),
]);
throw new CrmException('Failed to fetch property');
}
return $result;
}
/**
* @return array<CrmFieldOption>
*/
public function fetchPropertyOptions(string $objectType, string $propertyId): array
{
/** @var array<CrmFieldOption> */
return $this->fetchProperty($objectType, $propertyId)->getOptions();
}
/**
* @return array<array{id:string, label:string, deleted:bool}>
*/
public function fetchCallDispositions(): array
{
/** @var Response $response */
$response = $this->getInstance()->engagements()->getCallDispositions();
/**
* @var array<array{
* id:string,
* label:string,
* deleted: bool
* }>
*/
return $response->toArray();
}
/**
* @return array<CrmFieldOption>
*/
public function fetchOpportunityPipelineStages(): array
{
$stages = [];
$apiResponse = $this->getNewInstance()->crm()->pipelines()->pipelinesApi()->getAll('deals');
if ($apiResponse instanceof Error) {
$this->log->error('[Hubspot] Failed to fetch opportunity pipelines', [
'reason' => $apiResponse->getMessage(),
]);
return [];
}
foreach ($apiResponse->getResults() as $pipeline) {
$pipelineStages = array_map(
static function (PipelineStage $stage) {
return [
'id' => $stage->getId(),
'label' => $stage->getLabel(),
];
},
$pipeline->getStages()
);
$stages = array_merge($stages, $pipelineStages);
}
return $stages;
}
public function fetchOpportunityPipelines(): array
{
$pipelines = [];
try {
$apiResponse = $this->makeRequest('/crm/v3/pipelines/deals');
} catch (\Exception $e) {
$this->log->info('[Hubspot] Failed to fetch opportunity pipelines', [
'reason' => $e->getMessage(),
]);
return [];
}
$response = $apiResponse->toArray();
foreach ($response['results'] as $pipeline) {
$pipelines[] = [
'id' => $pipeline['id'],
'label' => $pipeline['label'],
];
}
return $pipelines;
}
/**
* @return array<CrmFieldOption>
*/
public function fetchMeetingOutcomeFieldOptions(Field $field): array
{
return $field->getCrmProviderId() === 'meetingOutcome'
? $this->fetchMeetingOutcomeTypes()
: $this->fetchCallActivityTypes();
}
public function fetchMeetingOutcomeTypes(): array
{
return $this->extractMeetingTypeOptions(
'[URL_WITH_CREDENTIALS] Response $response */
$response = $this->getInstance()
->getClient()
->request('GET', $endpoint);
/**
* @var array<array{
* value: string,
* label: string,
* displayOrder: int
* }> $optionData
*/
$optionData = $response->toArray()['options'] ?? [];
$options = [];
foreach ($optionData as $item) {
$options[] = [
'id' => $item['value'],
'value' => $item['value'],
'label' => $item['label'],
'display_order' => $item['displayOrder'],
];
}
return $options;
}
/**
* @return array<CrmFieldOption>
*/
public function fetchDispositionFieldOptions(): array
{
$options = [];
$dispositions = $this->fetchCallDispositions();
foreach ($dispositions as $disposition) {
if ($disposition['deleted'] !== false) {
continue;
}
$option['value'] = $disposition['id'];
$option['id'] = $disposition['id'];
$option['label'] = $disposition['label'];
$options[] = $option;
}
return $options;
}
/**
* @return array<CrmFieldOption>
*/
public function fetchOpportunityFieldOptions(Field $field): array
{
if ($field->isStageField()) {
return $this->fetchOpportunityPipelineStages();
}
if ($field->isPipelineField()) {
return $this->fetchOpportunityPipelines();
}
return $this->fetchPropertyOptions('deals', $field->getCrmProviderId());
}
/**
* @throws BadRequest
* @throws HubspotException
*/
public function makeRequest(string $endpoint, $method = 'GET', $payload = [], ?string $queryString = null)
{
$endpoint = self::BASE_URL . $endpoint;
if ($method === 'GET') {
$response = $this->getInstance()->getClient()?->request(
method: $method,
endpoint: $endpoint,
query_string: $queryString
);
} else {
$response = $this->getInstance()->getClient()->request($method, $endpoint, [
'json' => ($payload),
]);
}
return $response;
}
/**
* @throws BadRequest
* @throws HubspotException
*/
public function createMeeting(array $payload): Response
{
$endpoint = '/crm/v3/objects/meetings';
return $this->makeRequest($endpoint, 'POST', $payload);
}
/**
* @throws BadRequest
* @throws HubspotException
*/
public function updateMeeting(string $meetingId, array $payload): Response
{
$endpoint = '/crm/v3/objects/meetings/' . $meetingId;
return $this->makeRequest($endpoint, 'PATCH', $payload);
}
/**
* @throws \Exception
*/
public function createNote(
string $body,
string $ownerId,
int $timestamp,
string $objectId,
NoteObject $noteObject
): ?string {
try {
$noteInput = new SimplePublicObjectInput([
'properties' => [
'hs_note_body' => $body,
'hubspot_owner_id' => $ownerId,
'hs_timestamp' => $timestamp,
],
]);
// Create note
$note = $this->getNewInstance()->crm()->objects()->basicApi()->create('note', $noteInput);
$this->getNewInstance()->crm()->objects()->associationsApi()->create(
'note',
$note->getId(),
$this->getNoteObject($noteObject),
$objectId,
$this->getNoteAssociationType($noteObject),
);
return $note->getId();
} catch (\Exception $e) {
$this->log->error('[Hubspot] Failed to create note', [
'objectId' => $objectId,
'noteObject' => $noteObject->getObjectType(),
'reason' => $e->getMessage(),
]);
\Sentry::captureException($e);
}
return null;
}
public function updateEngagement(string $objectId, array $engagement, array $metadata): void
{
$this->getInstance()->engagements()->update($objectId, $engagement, $metadata);
}
public function getEngagementData(string $engagementId): array
{
$engagement = $this->getInstance()->engagements()->get($engagementId);
return $engagement->toArray();
}
public function createEngagement(array $engagement, array $associations, array $metadata): Response
{
return $this->getInstance()
->engagements()
->create($engagement, $associations, $metadata);
}
public function isUnauthorizedException(\Exception $e): bool
{
// Check for specific HubSpot API exception types first
if ($e instanceof BadRequest) {
// BadRequest can contain 401 status codes
return $e->getCode() === 401;
}
// Check for HTTP client exceptions with status codes
if ($e instanceof \GuzzleHttp\Exception\RequestException && $e->hasResponse()) {
$response = $e->getResponse();
if ($response !== null) {
return $response->getStatusCode() === 401;
}
}
// Check for Guzzle HTTP exceptions
if ($e instanceof \GuzzleHttp\Exception\ClientException) {
return $e->getCode() === 401;
}
// Fallback to string matching as last resort, but be more specific
$message = strtolower($e->getMessage());
return str_contains($message, '401 unauthorized') ||
str_contains($message, 'http 401') ||
str_contains($message, 'status code 401') ||
(preg_match('/\b401\b/', $message) && str_contains($message, 'unauthorized'));
}
/**
* Validates and refreshes the access token if needed before API requests.
* This ensures long-running processes don't fail due to token expiration.
*
* @throws SocialAccountTokenInvalidException
*/
public function ensureValidToken(): void
{
if ($this->oauthAccount === null) {
return;
}
$newToken = $this->tokenManager->ensureValidToken($this->oauthAccount);
if ($newToken !== null) {
$this->accessToken = $newToken;
}
}
public function getConfig()
{
return $this->config;
}
// returns only active (archived=false)
public function getOwners(): array
{
return $this->getNewInstance()->crm()->owners()->getAll();
}
/**
* @param bool $archived
*
* @return array<Owner>|[]
*/
public function getOwnersArchived(bool $archived = true): array
{
$endpoint = '/crm/v3/owners';
$queryParams = [
'archived' => $archived ? 'true' : 'false',
];
$queryString = http_build_query($queryParams);
$owners = [];
try {
$response = $this->makeRequest(endpoint: $endpoint, queryString: $queryString);
$responseData = $response?->toArray();
foreach ($responseData['results'] as $result) {
try {
$owners[] = Owner::create($result);
} catch (Throwable $e) {
$this->log->error('[HubSpot] Failed to process owner data', [
'result' => $result,
'error' => $e->getMessage(),
]);
continue;
}
}
} catch (Throwable $e) {
$this->log->error('HubSpot] Failed to fetch owners', [
'archived' => $archived,
'error' => $e->getMessage(),
]);
return [];
}
return $owners;
}
public function getMeeting(string $engagementId): ObjectWithAssociations
{
return $this->getNewInstance()->crm()->objects()->basicApi()
->getById('meeting', $engagementId, null, 'contact,company,deal');
}
public function deleteEngagement(string $engagementId): void
{
$this->getInstance()->engagements()->delete((int) $engagementId);
}
public function getAssociationsData(array $ids, string $fromObject, string $toObject): array
{
$associationData = [];
$idChunks = array_chunk($ids, self::ASSOCIATIONS_BATCH_SIZE_LIMIT);
foreach ($idChunks as $idChunk) {
try {
$batchInput = new \HubSpot\Client\Crm\Associations\Model\BatchInputPublicObjectId();
$batchInput->setInputs(array_map(function ($id) {
$publicObjectId = new \HubSpot\Client\Crm\Associations\Model\PublicObjectId();
$publicObjectId->setId($id);
return $publicObjectId;
}, $idChunk));
$associatedObjectsData = $this
->getNewInstance()
->crm()
->associations()
->batchApi()
->read($fromObject, $toObject, $batchInput);
if ($associatedObjectsData instanceof \HubSpot\Client\Crm\Associations\Model\BatchResponsePublicAssociationMulti) {
foreach ($associatedObjectsData->getResults() as $association) {
$from = $association->getFrom()->getId();
$toAssociations = $association->getTo();
if (! empty($toAssociations)) {
$associationData[$from] = array_map(function ($item) {
return $item->getId();
}, $toAssociations);
}
}
}
} catch (\Exception $e) {
$this->log->error('[Hubspot] Failed to fetch associations', [
'from_object' => $fromObject,
'to_object' => $toObject,
'reason' => $e->getMessage(),
]);
}
}
return $associationData;
}
/**
* @throws \Exception
*/
private function getNoteAssociationType(NoteObject $noteObject): string
{
return match($noteObject) {
NoteObject::Opportunity => 'note_to_deal',
NoteObject::Lead, NoteObject::Contact => 'note_to_contact', // or 'note_to_lead' if your portal supports it
NoteObject::Account => 'note_to_company',
NoteObject::Call, NoteObject::Event => throw new \Exception('Not supported'),
};
}
/**
* @throws \Exception
*/
private function getNoteObject(NoteObject $noteObject): string
{
return match($noteObject) {
NoteObject::Opportunity => 'deal',
NoteObject::Lead, NoteObject::Contact => 'contact',
NoteObject::Account => 'company',
NoteObject::Call, NoteObject::Event => throw new \Exception('Not supported'),
};
}
public function addAssociations(string $objectType, string $associationType, array $payload): Response
{
$endpoint = "/crm/v4/associations/$objectType/$associationType/batch/create";
return $this->makeRequest($endpoint, 'POST', $payload);
}
public function removeAssociations(string $objectType, string $associationType, array $payload): Response
{
$endpoint = "/crm/v4/associations/$objectType/$associationType/batch/archive";
return $this->makeRequest($endpoint, 'POST', $payload);
}
}
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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:
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hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
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Give positive feedback
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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?
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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...
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Claude
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Claude
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NULL
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