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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
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Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
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You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
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Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
Copy to clipboard
php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.
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You said: Ok, so what will happen each time I make a request?
You said: Ok, so what will happen each time I make a request?
Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?
13:44
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Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Analyzed per-request overhead concerns and quantified Redis performance impact
Analyzed per-request overhead concerns and quantified Redis performance impact
Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out. But the overhead is much smaller than it sounds, and importantly, it's nowhere near your bottleneck.
What "two buckets per request" actually costs
What "two buckets per request" actually costs
Each request makes
one round trip to Redis
— not two — because you fold both bucket checks into a single Lua script that runs atomically server-side:
Copy to clipboard
lua
-- KEYS[1] = burst bucket key (or search)
-- KEYS[2] = daily bucket key
-- ARGV[1] = now (ms) ARGV[2] = window_ms
-- ARGV[3] = bucket_max ARGV[4] = daily_max
-- ARGV[5] = request_id ARGV[6] = daily_ttl
-- Trim sliding window
redis
.
call
(
'ZREMRANGEBYSCORE'
,
KEYS
[
1
]
,
0
,
ARGV
[
1
]
-
ARGV
[
2
]
)
local
burst_used
=
redis
.
call
(
'ZCARD'
,
KEYS
[
1
]
)
local
daily_used
=
tonumber
(
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
|
1970
|
|
1969
|
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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
Retry
Edit
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Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
Copy to clipboard
php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.
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You said: Ok, so what will happen each time I make a request?
You said: Ok, so what will happen each time I make a request?
Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?
13:44
Retry
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Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Analyzed per-request overhead concerns and quantified Redis performance impact
Analyzed per-request overhead concerns and quantified Redis performance impact
Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out. But the overhead is much smaller than it sounds, and importantly, it's nowhere near your bottleneck.
What "two buckets per request" actually costs
What "two buckets per request" actually costs
Each request makes
one round trip to Redis
— not two — because you fold both bucket checks into a single Lua script that runs atomically server-side:
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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...
|
Claude
|
Claude
|
NULL
|
1969
|
|
1968
|
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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
Show more
10:42
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Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
Copy
Give positive feedback
Give negative feedback
Retry
You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
Retry
Edit
Copy
Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:...
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Claude
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1967
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SELECT * FROM teams WHERE name LIKE '%litify%'; # 1069, 994, 24993
SELECT * FROM users WHERE id = 25061;
SELECT * FROM crm_profiles WHERE crm_configuration_id = 994;
SELECT * FROM crm_profiles WHERE user_id = 25061;
select * from crm_configurations where id = 834;
SELECT * FROM teams WHERE id = 882;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 882 and sa.provider = 'hubspot';
SELECT * FROM crm_configurations where id = 882; # 933 - GoGlobal
SELECT * FROM opportunities WHERE team_id = 933 order by updated_at desc;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 933 and sa.provider = 'hubspot';
SELECT * FROM crm_configurations WHERE provider = 'hubspot' and crm_provider_id = 7270388;
SELECT * FROM contacts where crm_configuration_id = 834;
SELECT * FROM opportunities WHERE team_id = 933
# AND crm_provider_id IN ('20131586060','46017317898','52543911090','53451356564','54101251892','54323768459');
AND id IN (8482561,18352941,19042734,19232139,19445140,19472541);
SELECT * FROM opportunity_contacts
WHERE opportunity_id IN (8482561,18352941,19042734,19232139,19445140,19472541);
# [PASSWORD_DOTS]
SELECT * FROM crm_configurations where id = 485; #
SELECT * FROM opportunities WHERE team_id = 933 order by updated_at desc;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 933 and sa.provider = 'hubspot';
select crm.provider, l.* from leads l join crm_configurations crm on l.crm_configuration_id = crm.id
where crm.provider NOT IN ('salesforce', 'integration-app', 'bullhorn', 'copper')
# and l.converted_at IS NOT NULL
;
# [PASSWORD_DOTS]
SELECT * FROM activities a WHERE type IN ('email-inbound', 'email-outbound')
and opportunity_id IS NULL
order by id desc;
SELECT * FROM teams WHERE id = 604; # 598
SELECT * FROM activities WHERE id = 74410828; # [EMAIL]
SELECT * FROM accounts WHERE id = 20068382;
SELECT * FROM accounts WHERE id = 35186038;
SELECT * FROM contacts WHERE team_id = 852 and updated_at > '2026-01-23 12:30:00' order by updated_at desc;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 559 and sa.provider = 'hubspot';
SELECT * FROM activities WHERE uuid_to_bin('cb6342b6-a183-401c-b0af-ede92b2ae763') = uuid;
select * from sidekick_settings where team_id = 781;
SELECT * FROM crm_configurations where provider = 'hubspot' and crm_provider_id = 26651871; # Teya
SELECT * FROM crm_configurations where provider = 'hubspot' and crm_provider_id = 7562435;
SELECT * FROM crm_configurations where provider = 'hubspot' and crm_provider_id = 8420347; # opflit 2100
SELECT * FROM crm_layouts WHERE crm_configuration_id = 711;
SELECT * FROM activities where crm_configuration_id = 711 and crm_provider_id IS NULL
and is_internal = 0 and status = 'completed'
order by id desc;
SELECT * FROM crm_layout_entities
WHERE crm_layout_id IN (2352, 2353);
;
SELECT * FROM crm_configurations where provider = 'hubspot' and id = 530;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 556 and sa.provider = 'hubspot';
SELECT * FROM activities WHERE uuid_to_bin('c6ca4b22-7738-4563-a95d-b8a9598924ae') = uuid;
SELECT * FROM activities WHERE uuid_to_bin('442abb2b-28bd-4be8-9c25-19e9bf02766d') = uuid;
select * from contacts
where crm_configuration_id = 530
and crm_provider_id = 872252;
select * from activities where crm_configuration_id = 530
and user_id = 14343 and type like '%softphone%'
and created_at between '2026-01-28 15:00:00' and '2026-01-28 15:10:00';
SELECT * FROM crm_configurations where provider = 'hubspot' and crm_provider_id = 25666868; # Teya
SELECT * FROM crm_configurations where provider = 'hubspot' and crm_provider_id = 8646335; # Teya
SELECT * FROM crm_configurations where provider = 'hubspot' and crm_provider_id IN (5933397);
SELECT t.name, t.id, t.owner_id, c.id, c.provider, c.crm_base_url FROM teams t
JOIN crm_configurations c ON t.id = c.team_id
WHERE t.status = 'active';
SELECT * FROM teams where id = 1091;
SELECT * FROM crm_configurations where team_id = 1091;
SELECT * FROM activity_providers where team_id = 1091;
SELECT * FROM activities where crm_configuration_id = 1024 and type IN ('softphone', 'softphone-outbound')
and provider NOT IN ('hubspot', 'aircall')
# and telephony_provider_id = '019c1131-a22f-4792-b9ea-20adf6a02ed0'
order by id desc;
SELECT * FROM teams WHERE name LIKE '%Leadventure%';
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 1091 and sa.provider = 'salesforce';
SELECT * FROM teams WHERE name LIKE '%Wilson%'; # 862, 812
SELECT * FROM teams where id = 862;
SELECT * FROM crm_configurations where team_id = 862;
SELECT * FROM activity_providers where team_id = 862;
SELECT * FROM activities where crm_configuration_id = 812 and type IN ('softphone', 'softphone-outbound')
and provider NOT IN ('hubspot', 'aircall')
# and telephony_provider_id = '019c1131-a22f-4792-b9ea-20adf6a02ed0'
order by id desc;
SELECT t.id, crm.id, crm.provider, ap.* FROM teams t
join crm_configurations crm on t.id = crm.team_id
join activity_providers ap on t.id = ap.team_id
where t.status = 'active' and ap.is_enabled = 1
and crm.provider = 'hubspot'
and ap.provider NOT IN ('hubspot', 'aircall', 'uploader', 'gong', 'twilio', 'zoom-bot', 'google-meet', 'ms-teams',
'outreach', 'close', 'ringcentral', 'dialpad', 'zoom-phone');
SELECT * FROM teams where id = 1068;
SELECT * FROM crm_configurations where team_id = 1068;
SELECT * FROM activity_providers where team_id = 1068;
SELECT * FROM activities a
where crm_configuration_id = 993 and type IN ('softphone', 'softphone-outbound')
and a.provider NOT IN ('hubspot', 'uploader', 'gong', 'twilio', 'google-meet', 'ms-teams','close'
)
# and telephony_provider_id = '019c1131-a22f-4792-b9ea-20adf6a02ed0'
order by a.id desc;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 1068 and sa.provider = 'hubspot';
# [PASSWORD_DOTS]
# [PASSWORD_DOTS]
# [PASSWORD_DOTS]
SELECT * FROM crm_configurations where id = 882; # 933 - GoGlobal , portalId: 6017093
SELECT * FROM opportunities WHERE team_id = 933 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 933 and updated_at > '2026-02-06 00:00:00' order by updated_at desc;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 933 and sa.provider = 'hubspot';
# [PASSWORD_DOTS]
SELECT * FROM crm_configurations where id = 834; # 882 - AnyVan , portalId: 5468262
SELECT * FROM contacts WHERE crm_configuration_id = 834 and updated_at > '2026-03-30 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE crm_configuration_id = 834 and updated_at > '2026-03-04 08:00:00' order by updated_at desc;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 882 and sa.provider = 'hubspot';
select * from crm_layouts where crm_configuration_id = 834;
select * from crm_layout_entities where crm_layout_id = 2780;
select * from crm_fields where id IN (321153,321192,321193,321194);
SELECT * FROM opportunities WHERE crm_configuration_id = 834 and id = 10993426;
# [PASSWORD_DOTS]
SELECT * FROM crm_configurations where id = 988; # 1057 - Teya (543ce4f4-168c-4571-91ea-5b35c253f06f) , portalId: 26651871
SELECT * FROM opportunities WHERE team_id = 1057 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 1057 and updated_at > '2026-02-04 00:00:00' order by updated_at desc;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 1057 and sa.provider = 'hubspot';
SELECT * FROM crm_configurations where id = 533; # 559 - Connectd , portalId: 6710988
SELECT * FROM opportunities WHERE team_id = 559 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 559 and updated_at > '2026-02-04 00:00:00' order by updated_at desc;
SELECT * FROM crm_configurations where id = 801; # 852 - Rise Vision , portalId: 2700250
SELECT * FROM opportunities WHERE team_id = 852 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 852 and updated_at > '2026-02-04 00:00:00' order by updated_at desc; # 6th last
SELECT * FROM crm_configurations where id = 962; # 1034 - evergrowth.io , portalId: 143180990
SELECT * FROM opportunities WHERE team_id = 1034 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 1034 and updated_at > '2026-02-04 00:00:00' order by updated_at desc;
# [PASSWORD_DOTS]
SELECT * FROM crm_configurations where id = 1037; # 1102 - Jibble , portalId: 6649755
SELECT * FROM opportunities WHERE team_id = 1102 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 1102 and updated_at > '2026-02-04 12:30:00' order by updated_at desc; # 8
SELECT * FROM crm_configurations where id = 1015; # 1049 - Travefy , portalId: 48904401
SELECT * FROM opportunities WHERE team_id = 1049 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 1049 and updated_at > '2026-02-04 12:30:00' order by updated_at desc; # 20
SELECT * FROM crm_configurations where id = 64; # 70 - SalaryFinance , portalId: 3404115
SELECT * FROM opportunities WHERE team_id = 70 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 70 and updated_at > '2026-02-04 12:30:00' order by updated_at desc; # 6th last
SELECT * FROM crm_configurations where id = 802; # 853 - Street Group , portalId: 7658438
SELECT * FROM opportunities WHERE team_id = 853 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 853 and updated_at > '2026-02-04 12:30:00' order by updated_at desc; # 10
SELECT * FROM crm_configurations where id = 872; # 921 - In Professional Development , portalId: 9238273
SELECT * FROM opportunities WHERE team_id = 921 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 921 and updated_at > '2026-02-04 12:30:00' order by updated_at desc; # 2
# [PASSWORD_DOTS]
SELECT * FROM crm_configurations where id = 550; # 576 - SeedLegals , portalId: 3028661
SELECT * FROM opportunities WHERE team_id = 576 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 576 and updated_at > '2026-02-05 14:00:00' order by updated_at desc;
SELECT * FROM crm_configurations where id = 989; # 1058 - rtaoutdoor.com , portalId: 22371204
SELECT * FROM opportunities WHERE team_id = 1058 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 1058 and updated_at > '2026-02-05 14:00:00' order by updated_at desc;
SELECT * FROM crm_configurations where id = 896; # 946 - Mintago , portalId: 6621281
SELECT * FROM opportunities WHERE team_id = 946 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 946 and updated_at > '2026-02-05 14:00:00' order by updated_at desc;
SELECT * FROM crm_configurations where id = 617; # 641 - PCS , portalId: 5244937
SELECT * FROM opportunities WHERE team_id = 641 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 641 and updated_at > '2026-02-05 14:00:00' order by updated_at desc; # 7th
# [PASSWORD_DOTS]
SELECT * FROM crm_configurations where id = 649; # 670 - Eventeny , portalId: 4492849
SELECT * FROM opportunities WHERE team_id = 670 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 670 and updated_at > '2026-02-09 08:00:00' order by updated_at desc; #
SELECT * FROM crm_configurations where id = 48; # 51 - CleanCloud , portalId: 4373137
SELECT * FROM opportunities WHERE team_id = 51 and updated_at > '2026-03-04 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 51 and updated_at > '2026-02-09 08:00:00' order by updated_at desc;
select * from users where team_id = 51; # 7783
SELECT * FROM groups WHERE uuid_to_bin('8a8d2cb6-8b55-4fa3-8b5c-5f0e3d8de59a') = uuid; # 1130
select * from activity_searches where user_id = 7783;
select * from activity_search_filters where activity_search_id IN (32291, 32292);
SELECT asf.activity_search_id, asf.id, asf.value
FROM activity_search_filters asf
WHERE asf.filter = 'group_id'
AND asf.value IN (
SELECT CONCAT(
HEX(SUBSTR(uuid, 5, 4)), '-',
HEX(SUBSTR(uuid, 3, 2)), '-',
HEX(SUBSTR(uuid, 1, 2)), '-',
HEX(SUBSTR(uuid, 9, 2)), '-',
HEX(SUBSTR(uuid, 11))
)
FROM groups
WHERE deleted_at IS NOT NULL
);
SELECT * FROM crm_configurations where id = 272; # 290 - Bonham & Brook , portalId: 5705856
SELECT * FROM opportunities WHERE team_id = 290 and updated_at > '2026-02-05 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 290 and updated_at > '2026-02-09 08:00:00' order by updated_at desc; # 6th
# [PASSWORD_DOTS]
SELECT * FROM crm_configurations where provider = 'hubspot';
SELECT * FROM crm_configurations where id = 1056; # 1119 - Chromatic , portalId: 45602133
SELECT * FROM opportunities WHERE team_id = 1119 and remotely_created_at > '2026-02-01 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 1119 and updated_at > '2026-02-09 09:00:00' order by updated_at desc; # null
# [PASSWORD_DOTS]
select * from contacts where crm_provider_id = '003Uu00000ojD4NIAU';
select
cp.*
# DISTINCT t.id
# cp.id, cp.user_id, t.id, cp.crm_configuration_id, cp.contact_fields
FROM crm_profiles cp
JOIN crm_configurations crm on crm.id = cp.crm_configuration_id
JOIN users u on u.id = cp.user_id
JOIN teams t ON t.id = crm.team_id
WHERE crm.provider = 'salesforce' and t.status = 'active'
and cp.archived_at IS NULL and u.deleted_at IS NULL
and t.id NOT IN (1093)
and t.id = 2
and cp.contact_fields IS NULL;
# and c.crm_provider_id = '003Uu00000ojD4NIAU';
SELECT * FROM users WHERE id = 26484;
SELECT * FROM crm_profiles WHERE user_id = 26484;
SELECT * FROM social_accounts WHERE sociable_id = 26484;
SELECT * FROM crm_configurations where provider = 'salesforce';
select * from users where id IN (10022, 10403);
select * from users where team_id IN (526);
select * from teams where id IN (526, 532);
select * from crm_configurations where id IN (500, 516);
select * from crm_profiles where crm_configuration_id IN (500, 516) and user_id IN (10022, 10403);
select * from contacts where crm_configuration_id IN (500, 516) and crm_provider_id = '003Uu00000ojD4NIAU';
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 526 and sa.provider = 'salesforce';
select * from team_settings where team_id IN (526, 532);
select * from users where id IN (22824);
select * from crm_profiles where crm_configuration_id IN (1026);
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 1093 and sa.provider = 'salesforce';
select * from teams where id = 1099;
select * from users where id = 29643
select * from activity_processing_states;
SELECT * FROM teams where name LIKE '%Fare%'; # 233
SELECT * FROM opportunities where crm_configuration_id = 215
# and crm_provider_id = 'oppo_ogESZf2P50nDrd1nGPvKDXeA6sSaTN5v51Lp4ayVzKR'
;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 1088 and sa.provider = 'hubspot';
SELECT * FROM teams order by updated_at DESC
SELECT * FROM crm_configurations WHERE id = 1019; # SimpleConsign 1088 - no social account
select * from crm_configurations where provider = 'pipedrive';
select * from teams where id = 957;
select * from crm_configurations where id = 957;
SELECT * FROM teams WHERE name LIKE '%Prolific%'; # 544, 518, 10743
SELECT * FROM opportunities where crm_configuration_id = 518 order by id desc;
select * from users where team_id = 1; # 26726 - Gabriela Dureva
SELECT * FROM opportunities where user_id = 26726; # 16834447 - Prolific
select * from activities where user_id = 26726 order by id desc;
select * from contacts where crm_configuration_id = 1
and email IN ('[EMAIL]', '[EMAIL]'); # 2094416, 2093620
SELECT * FROM contacts WHERE id = 6284931;
SELECT p.* FROM activities a JOIN participants p ON a.id = p.activity_id
WHERE a.user_id = 26726 and p.lead_id IN (2094416, 2093620) and a.created_at > '2026-01-01 00:00:00' order by p.email;
select * from activities where id IN (75509259,75509261,75509261,75511034,75026464,75517602,75517605);
select * from crm_configurations where id = 1;
43801692-1aeb-32ce-acba-5b80a479701a
44c3c9cf-6f5e-75f3-8179-bc9f75dd2b1b
405975c0-b3d0-7aaa-821f-09d59cae6dd1
4caf848d-4bed-2299-b248-7788d41f9fca
49bedc3f-f196-eef3-89c3-dea6a3b4aa63
43420989-a09d-b8f8-9806-c8bbf7a02aac
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 1 and sa.provider = 'salesforce';
SELECT * FROM activities WHERE id = 75461988;
SELECT * FROM activities WHERE uuid_to_bin('d6c5052e-e972-49e9-8912-26f2f7d6c5f6') = uuid;
select * from contacts where id = 17900517;
select * from contact_roles cr join crm_configurations crm on cr.crm_configuration_id = crm.id
where crm.provider != 'salesforce';
select * from users where id = 21047;
SELECT * FROM crm_configurations WHERE id = 892;
SELECT * FROM teams WHERE id = 942;
select * from opportunities where team_id = 942 order by updated_at desc;
select * from contacts where team_id = 942 order by updated_at desc;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 942 and sa.provider = 'hubspot';
SELECT * FROM opportunities where team_id = 1 and crm_provider_id IN ('006Pq00000NeH6XIAV', '006Pq000007z8kdIAA'); # 10697889, 6621430
SELECT * FROM crm_configurations WHERE id = 1;
SELECT * FROM teams WHERE crm_id = 1;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 1 and sa.provider = 'salesforce';
select id, user_id, opportunity_fields from crm_profiles where crm_configuration_id = 1
SELECT * FROM opportunities where team_id = 1 order by updated_at desc; # 10697889, 6621430
select * from teams where id = 852;
select * from groups where id = 2286;
select * from sidekick_settings where team_id = 852;
select * from default_activity_types where team_id = 852;
SELECT cc.provider, cc.id, p.id, u.*
FROM users u
LEFT JOIN crm_profiles p ON u.id = p.user_id AND p.id IS NULL -- no profile
INNER JOIN teams t ON u.team_id = t.id AND t.status = 'active' -- team is active
INNER JOIN crm_configurations cc ON t.crm_id = cc.id
WHERE u.status = 1 AND u.deleted_at IS NULL
AND u.crm_required = 1
AND u.team_id = 1
ORDER BY u.team_id;
SELECT * FROM crm_profiles cp where cp.crm_configuration_id = 1 and cp.user_id IN (
18481
);
SELECT cc.provider, cc.id, p.id, u.*
FROM users u
LEFT JOIN crm_profiles p ON u.id = p.user_id
INNER JOIN teams t ON u.team_id = t.id AND t.status = 'active'
INNER JOIN crm_configurations cc ON t.crm_id = cc.id
WHERE u.status = 1
AND u.deleted_at IS NULL
AND u.crm_required = 1
# AND u.team_id = 1
AND p.id IS NULL -- Move this condition to WHERE clause
ORDER BY u.team_id;
SELECT * FROM opportunities WHERE id = 20002609;
select * from teams where id = 1122; # Velatir, 29953 - [EMAIL]
select * from crm_configurations where id = 1060;
select * from crm_layouts where crm_configuration_id = 1060;
SELECT * FROM crm_layout_entities WHERE crm_layout_id = 3596;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 1122 and sa.provider = 'hubspot';
select * from opportunities where team_id = 1122 order by updated_at desc;
select * from crm_field_data where object_type = 'contact';
SELECT * FROM activities WHERE uuid_to_bin('374fc8ed-3315-4c9f-9b25-318b7fd2928f') = uuid; # 76584262
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 248 and sa.provider = 'salesforce';
SELECT * FROM crm_profiles where user_id = 24115; # 005QF000002CswMYAS
SELECT * FROM users where id = 24115;
SELECT * FROM accounts where id = 4002896;
SELECT * FROM teams WHERE name LIKE '%adswerve%';
SELECT * FROM opportunities where crm_configuration_id = 230 AND crm_provider_id IN ("0069N000003GIQ9QAO","0061r000019yGP9AAM","0066900001S2KWlAAN","0066900001TDpj2AAD","0066900001b8uEwAAI","0069N000001rQi0QAE","006QF00000KD40mYAD","006QF00000LzpRJYAZ","0069N000002uomtQAA","0069N000002xlMLQAY","0066900001NV6ubAAD","0061r00001HJp45AAD","006QF00000uTlUoYAK","006QF00000v0bZqYAI");
SELECT * FROM opportunities WHERE crm_configuration_id = 230 AND crm_provider_id = '0069N000003GIQ9QAO'; # 6272203
SELECT u.id, u.email, ac.name, a.* FROM activities a
JOIN users u ON a.user_id = u.id
JOIN accounts ac ON a.account_id = ac.id
WHERE
uuid_to_bin('e3269598-b562-44fb-b5e9-9d2694dc63e0') = a.uuid or
uuid_to_bin('66ddc3ab-4e15-45aa-af0c-248c1eece593') = a.uuid or
uuid_to_bin('826bd328-e1cc-4213-b8d8-572454cacc07') = a.uuid;
select * from users where id = 5825;
SELECT * FROM activities WHERE uuid_to_bin('e56aa2e8-231a-421b-ab1f-cb38ed2bf573') = uuid;
select * from activities where uuid_to_bin('91e13b2f-2d1b-45f8-b1fd-1141b6563782') = uuid;
19594, 862
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 862 and sa.provider = 'salesforce';
select * from automated_reports where id = 36;
select ar.frequency, r.*, ar.* from automated_report_results r
join automated_reports ar on r.report_id = ar.id
where ar.frequency != 'one_off';
select s.* from activity_searches s join users u ON s.user_id = u.id where u.team_id = 882;
select * from nudges n where n.activity_search_id
select * from teams where created_at > '2026-03-09';
SELECT * FROM crm_layouts WHERE crm_configuration_id = 1065; # 1065
SELECT * FROM crm_layout_entities WHERE crm_layout_id = 3617;
select * from users where team_id = 1 and name like '%Lukas%'; # 7160
SELECT * FROM teams WHERE id = 575;
select * from opportunities where team_id = 575;
SELECT * FROM teams WHERE name LIKE '%Integrum ESG%'; # 1126, 1065,
select * from opportunities where team_id = 1126;
SELECT * FROM teams WHERE name LIKE '%Base%'; # 1125, 1063,
select * from opportunities where team_id = 1125;
select * from contacts c
where c.team_id = 882;
SELECT * FROM activities WHERE id = 76822967;
SELECT * FROM crm_profiles WHERE user_id = 15440;
SELECT * FROM crm_profiles WHERE crm_configuration_id = 555;
SELECT * FROM crm_configurations WHERE id = 555;
SELECT * FROM users WHERE id = 15440; # team. 581, gr. 15440, pl. 3911, act. field 162182
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 581 and sa.provider = 'salesforce';
SELECT * FROM automated_report_results order by id desc;
select * from features;
select * from team_features where feature_id = 40;
select * from teams where id = 556;
select * from automated_reports;
where id = 54; # 4fdd41f6-dcf0-30d0-b339-7345381b6044 , ["pdf","podcast"]
SELECT * FROM automated_report_results WHERE uuid_to_bin('822fa41b-afd3-43a9-a248-86b0e36f3131') = uuid;
select * from automated_report_results order by id desc;
SELECT * FROM automated_report_results WHERE id = 1919;
select * from automated_report_results WHERE report_id = 54;
select * from opportunities where id = 7594349;
SELECT * FROM teams WHERE name LIKE '%Les%'; # 711, 692, 16067 - [EMAIL]
select * from playbooks where team_id = 711; # event 226147
SELECT * FROM playbook_categories WHERE playbook_id = 5515;
SELECT * FROM crm_fields WHERE crm_configuration_id = 692 and object_type = 'event';
SELECT * FROM crm_fields WHERE id = 226147;
SELECT * FROM crm_field_values WHERE crm_field_id = 226147;
SELECT * FROM crm_configurations WHERE id = 692;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 711 and sa.provider = 'salesforce';
SELECT * FROM crm_profiles cp JOIN users u on u.id = cp.user_id WHERE u.team_id = 711;
select * from leads;
select * from calendars;
SELECT
t.id AS team_id,
t.name,
LOWER(SUBSTRING_INDEX(c.calendar_provider_id, '@', -1)) AS calendar_domain
FROM teams t
JOIN users u ON u.team_id = t.id
JOIN calendars c ON c.user_id = u.id AND c.status = 'active' AND c.calendar_provider_id LIKE '%@%'
LEFT JOIN team_domains td
ON td.team_id = t.id
AND td.deleted_at IS NULL
AND td.domain = LOWER(SUBSTRING_INDEX(c.calendar_provider_id, '@', -1))
GROUP BY t.id, t.name, calendar_domain
ORDER BY t.name, calendar_domain;
select * from users u join calendars c on c.user_id = u.id
where u.team_id = 882;
select * from activities where id = 74049485; # team 563 crm 537
select * from activities where id = 73272382; # team 563 crm 537
select * from activities where id = 64400389; # team 563 crm 537
select * from activities where id = 58081273; # team 563 crm 537
select * from activities where id = 54520297; # team 563 crm 537
select * from participants where activity_id = 58081273;
select * from activities where crm_configuration_id = 537 and provider = 'aircall'
and account_id = 19003658 order by updated_at desc;
select * from contacts where crm_configuration_id = 537 and id = 35957759;
select * from accounts where crm_configuration_id = 537 and id = 19003658;
select * from automated_report_results where id = 1976;
select * from automated_reports where id = 583;
select * from activity_searches where id = 87714;
select * from activity_search_filters where activity_search_id = 87714;
SELECT * FROM activities WHERE uuid_to_bin('8827f672-202d-4162-9d04-73ff5f0566a9') = uuid
or uuid_to_bin('47842446-af51-4bcb-854f-cc6560290101') = uuid;
SELECT * FROM crm_configurations WHERE provider = 'hubspot';
select * from rate_limits;
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<?php
declare(strict_types=1);
namespace Jiminny\Models;
use Illuminate\Database\Eloquent\Relations\MorphTo;
use Jiminny\Component\Eloquent\Builder;
use Jiminny\Contracts\Http\RateLimitInterface;
/**
* Jiminny\Models\RateLimit
*
* @property int $id
* @property \Illuminate\Support\Carbon|null $created_at
* @property \Illuminate\Support\Carbon|null $updated_at
* @property int $quota
* @property int $window
* @property int $limited_id
* @property string $limited_type
* @property-read \Illuminate\Database\Eloquent\Model $limited
*
* @method static Builder|RateLimit chunkByIdDesc($count, callable $callback, $column = null, $alias = null)
* @method static Builder|RateLimit newModelQuery()
* @method static Builder|RateLimit newQuery()
* @method static Builder|RateLimit query()
* @method static Builder|RateLimit whereCreatedAt($value)
* @method static Builder|RateLimit whereId($value)
* @method static Builder|RateLimit whereLimitedId($value)
* @method static Builder|RateLimit whereLimitedType($value)
* @method static Builder|RateLimit whereQuota($value)
* @method static Builder|RateLimit whereUpdatedAt($value)
* @method static Builder|RateLimit whereWindow($value)
*
* @mixin \Eloquent
*/
class RateLimit extends Model implements RateLimitInterface
{
protected $fillable = [
'quota',
'window',
];
public function limited(): MorphTo
{
return $this->morphTo();
}
public function getQuota(): int
{
return $this->attributes['quota'];
}
public function getWindow(): int
{
return $this->attributes['window'];
}
public function getKey(): string
{
$key = sprintf(
'%s.%s.%s.%s',
$this->limited_id,
$this->limited_type,
$this->getQuota(),
$this->getWindow()
);
return base64_encode($key);
}
}
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SELECT * FROM teams WHERE name LIKE '%litify%'; # 1069, 994, 24993
SELECT * FROM users WHERE id = 25061;
SELECT * FROM crm_profiles WHERE crm_configuration_id = 994;
SELECT * FROM crm_profiles WHERE user_id = 25061;
select * from crm_configurations where id = 834;
SELECT * FROM teams WHERE id = 882;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 882 and sa.provider = 'hubspot';
SELECT * FROM crm_configurations where id = 882; # 933 - GoGlobal
SELECT * FROM opportunities WHERE team_id = 933 order by updated_at desc;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 933 and sa.provider = 'hubspot';
SELECT * FROM crm_configurations WHERE provider = 'hubspot' and crm_provider_id = 7270388;
SELECT * FROM contacts where crm_configuration_id = 834;
SELECT * FROM opportunities WHERE team_id = 933
# AND crm_provider_id IN ('20131586060','46017317898','52543911090','53451356564','54101251892','54323768459');
AND id IN (8482561,18352941,19042734,19232139,19445140,19472541);
SELECT * FROM opportunity_contacts
WHERE opportunity_id IN (8482561,18352941,19042734,19232139,19445140,19472541);
# [PASSWORD_DOTS]
SELECT * FROM crm_configurations where id = 485; #
SELECT * FROM opportunities WHERE team_id = 933 order by updated_at desc;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 933 and sa.provider = 'hubspot';
select crm.provider, l.* from leads l join crm_configurations crm on l.crm_configuration_id = crm.id
where crm.provider NOT IN ('salesforce', 'integration-app', 'bullhorn', 'copper')
# and l.converted_at IS NOT NULL
;
# [PASSWORD_DOTS]
SELECT * FROM activities a WHERE type IN ('email-inbound', 'email-outbound')
and opportunity_id IS NULL
order by id desc;
SELECT * FROM teams WHERE id = 604; # 598
SELECT * FROM activities WHERE id = 74410828; # [EMAIL]
SELECT * FROM accounts WHERE id = 20068382;
SELECT * FROM accounts WHERE id = 35186038;
SELECT * FROM contacts WHERE team_id = 852 and updated_at > '2026-01-23 12:30:00' order by updated_at desc;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 559 and sa.provider = 'hubspot';
SELECT * FROM activities WHERE uuid_to_bin('cb6342b6-a183-401c-b0af-ede92b2ae763') = uuid;
select * from sidekick_settings where team_id = 781;
SELECT * FROM crm_configurations where provider = 'hubspot' and crm_provider_id = 26651871; # Teya
SELECT * FROM crm_configurations where provider = 'hubspot' and crm_provider_id = 7562435;
SELECT * FROM crm_configurations where provider = 'hubspot' and crm_provider_id = 8420347; # opflit 2100
SELECT * FROM crm_layouts WHERE crm_configuration_id = 711;
SELECT * FROM activities where crm_configuration_id = 711 and crm_provider_id IS NULL
and is_internal = 0 and status = 'completed'
order by id desc;
SELECT * FROM crm_layout_entities
WHERE crm_layout_id IN (2352, 2353);
;
SELECT * FROM crm_configurations where provider = 'hubspot' and id = 530;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 556 and sa.provider = 'hubspot';
SELECT * FROM activities WHERE uuid_to_bin('c6ca4b22-7738-4563-a95d-b8a9598924ae') = uuid;
SELECT * FROM activities WHERE uuid_to_bin('442abb2b-28bd-4be8-9c25-19e9bf02766d') = uuid;
select * from contacts
where crm_configuration_id = 530
and crm_provider_id = 872252;
select * from activities where crm_configuration_id = 530
and user_id = 14343 and type like '%softphone%'
and created_at between '2026-01-28 15:00:00' and '2026-01-28 15:10:00';
SELECT * FROM crm_configurations where provider = 'hubspot' and crm_provider_id = 25666868; # Teya
SELECT * FROM crm_configurations where provider = 'hubspot' and crm_provider_id = 8646335; # Teya
SELECT * FROM crm_configurations where provider = 'hubspot' and crm_provider_id IN (5933397);
SELECT t.name, t.id, t.owner_id, c.id, c.provider, c.crm_base_url FROM teams t
JOIN crm_configurations c ON t.id = c.team_id
WHERE t.status = 'active';
SELECT * FROM teams where id = 1091;
SELECT * FROM crm_configurations where team_id = 1091;
SELECT * FROM activity_providers where team_id = 1091;
SELECT * FROM activities where crm_configuration_id = 1024 and type IN ('softphone', 'softphone-outbound')
and provider NOT IN ('hubspot', 'aircall')
# and telephony_provider_id = '019c1131-a22f-4792-b9ea-20adf6a02ed0'
order by id desc;
SELECT * FROM teams WHERE name LIKE '%Leadventure%';
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 1091 and sa.provider = 'salesforce';
SELECT * FROM teams WHERE name LIKE '%Wilson%'; # 862, 812
SELECT * FROM teams where id = 862;
SELECT * FROM crm_configurations where team_id = 862;
SELECT * FROM activity_providers where team_id = 862;
SELECT * FROM activities where crm_configuration_id = 812 and type IN ('softphone', 'softphone-outbound')
and provider NOT IN ('hubspot', 'aircall')
# and telephony_provider_id = '019c1131-a22f-4792-b9ea-20adf6a02ed0'
order by id desc;
SELECT t.id, crm.id, crm.provider, ap.* FROM teams t
join crm_configurations crm on t.id = crm.team_id
join activity_providers ap on t.id = ap.team_id
where t.status = 'active' and ap.is_enabled = 1
and crm.provider = 'hubspot'
and ap.provider NOT IN ('hubspot', 'aircall', 'uploader', 'gong', 'twilio', 'zoom-bot', 'google-meet', 'ms-teams',
'outreach', 'close', 'ringcentral', 'dialpad', 'zoom-phone');
SELECT * FROM teams where id = 1068;
SELECT * FROM crm_configurations where team_id = 1068;
SELECT * FROM activity_providers where team_id = 1068;
SELECT * FROM activities a
where crm_configuration_id = 993 and type IN ('softphone', 'softphone-outbound')
and a.provider NOT IN ('hubspot', 'uploader', 'gong', 'twilio', 'google-meet', 'ms-teams','close'
)
# and telephony_provider_id = '019c1131-a22f-4792-b9ea-20adf6a02ed0'
order by a.id desc;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 1068 and sa.provider = 'hubspot';
# [PASSWORD_DOTS]
# [PASSWORD_DOTS]
# [PASSWORD_DOTS]
SELECT * FROM crm_configurations where id = 882; # 933 - GoGlobal , portalId: 6017093
SELECT * FROM opportunities WHERE team_id = 933 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 933 and updated_at > '2026-02-06 00:00:00' order by updated_at desc;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 933 and sa.provider = 'hubspot';
# [PASSWORD_DOTS]
SELECT * FROM crm_configurations where id = 834; # 882 - AnyVan , portalId: 5468262
SELECT * FROM contacts WHERE crm_configuration_id = 834 and updated_at > '2026-03-30 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE crm_configuration_id = 834 and updated_at > '2026-03-04 08:00:00' order by updated_at desc;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 882 and sa.provider = 'hubspot';
select * from crm_layouts where crm_configuration_id = 834;
select * from crm_layout_entities where crm_layout_id = 2780;
select * from crm_fields where id IN (321153,321192,321193,321194);
SELECT * FROM opportunities WHERE crm_configuration_id = 834 and id = 10993426;
# [PASSWORD_DOTS]
SELECT * FROM crm_configurations where id = 988; # 1057 - Teya (543ce4f4-168c-4571-91ea-5b35c253f06f) , portalId: 26651871
SELECT * FROM opportunities WHERE team_id = 1057 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 1057 and updated_at > '2026-02-04 00:00:00' order by updated_at desc;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 1057 and sa.provider = 'hubspot';
SELECT * FROM crm_configurations where id = 533; # 559 - Connectd , portalId: 6710988
SELECT * FROM opportunities WHERE team_id = 559 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 559 and updated_at > '2026-02-04 00:00:00' order by updated_at desc;
SELECT * FROM crm_configurations where id = 801; # 852 - Rise Vision , portalId: 2700250
SELECT * FROM opportunities WHERE team_id = 852 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 852 and updated_at > '2026-02-04 00:00:00' order by updated_at desc; # 6th last
SELECT * FROM crm_configurations where id = 962; # 1034 - evergrowth.io , portalId: 143180990
SELECT * FROM opportunities WHERE team_id = 1034 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 1034 and updated_at > '2026-02-04 00:00:00' order by updated_at desc;
# [PASSWORD_DOTS]
SELECT * FROM crm_configurations where id = 1037; # 1102 - Jibble , portalId: 6649755
SELECT * FROM opportunities WHERE team_id = 1102 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 1102 and updated_at > '2026-02-04 12:30:00' order by updated_at desc; # 8
SELECT * FROM crm_configurations where id = 1015; # 1049 - Travefy , portalId: 48904401
SELECT * FROM opportunities WHERE team_id = 1049 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 1049 and updated_at > '2026-02-04 12:30:00' order by updated_at desc; # 20
SELECT * FROM crm_configurations where id = 64; # 70 - SalaryFinance , portalId: 3404115
SELECT * FROM opportunities WHERE team_id = 70 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 70 and updated_at > '2026-02-04 12:30:00' order by updated_at desc; # 6th last
SELECT * FROM crm_configurations where id = 802; # 853 - Street Group , portalId: 7658438
SELECT * FROM opportunities WHERE team_id = 853 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 853 and updated_at > '2026-02-04 12:30:00' order by updated_at desc; # 10
SELECT * FROM crm_configurations where id = 872; # 921 - In Professional Development , portalId: 9238273
SELECT * FROM opportunities WHERE team_id = 921 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 921 and updated_at > '2026-02-04 12:30:00' order by updated_at desc; # 2
# [PASSWORD_DOTS]
SELECT * FROM crm_configurations where id = 550; # 576 - SeedLegals , portalId: 3028661
SELECT * FROM opportunities WHERE team_id = 576 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 576 and updated_at > '2026-02-05 14:00:00' order by updated_at desc;
SELECT * FROM crm_configurations where id = 989; # 1058 - rtaoutdoor.com , portalId: 22371204
SELECT * FROM opportunities WHERE team_id = 1058 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 1058 and updated_at > '2026-02-05 14:00:00' order by updated_at desc;
SELECT * FROM crm_configurations where id = 896; # 946 - Mintago , portalId: 6621281
SELECT * FROM opportunities WHERE team_id = 946 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 946 and updated_at > '2026-02-05 14:00:00' order by updated_at desc;
SELECT * FROM crm_configurations where id = 617; # 641 - PCS , portalId: 5244937
SELECT * FROM opportunities WHERE team_id = 641 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 641 and updated_at > '2026-02-05 14:00:00' order by updated_at desc; # 7th
# [PASSWORD_DOTS]
SELECT * FROM crm_configurations where id = 649; # 670 - Eventeny , portalId: 4492849
SELECT * FROM opportunities WHERE team_id = 670 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 670 and updated_at > '2026-02-09 08:00:00' order by updated_at desc; #
SELECT * FROM crm_configurations where id = 48; # 51 - CleanCloud , portalId: 4373137
SELECT * FROM opportunities WHERE team_id = 51 and updated_at > '2026-03-04 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 51 and updated_at > '2026-02-09 08:00:00' order by updated_at desc;
select * from users where team_id = 51; # 7783
SELECT * FROM groups WHERE uuid_to_bin('8a8d2cb6-8b55-4fa3-8b5c-5f0e3d8de59a') = uuid; # 1130
select * from activity_searches where user_id = 7783;
select * from activity_search_filters where activity_search_id IN (32291, 32292);
SELECT asf.activity_search_id, asf.id, asf.value
FROM activity_search_filters asf
WHERE asf.filter = 'group_id'
AND asf.value IN (
SELECT CONCAT(
HEX(SUBSTR(uuid, 5, 4)), '-',
HEX(SUBSTR(uuid, 3, 2)), '-',
HEX(SUBSTR(uuid, 1, 2)), '-',
HEX(SUBSTR(uuid, 9, 2)), '-',
HEX(SUBSTR(uuid, 11))
)
FROM groups
WHERE deleted_at IS NOT NULL
);
SELECT * FROM crm_configurations where id = 272; # 290 - Bonham & Brook , portalId: 5705856
SELECT * FROM opportunities WHERE team_id = 290 and updated_at > '2026-02-05 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 290 and updated_at > '2026-02-09 08:00:00' order by updated_at desc; # 6th
# [PASSWORD_DOTS]
SELECT * FROM crm_configurations where provider = 'hubspot';
SELECT * FROM crm_configurations where id = 1056; # 1119 - Chromatic , portalId: 45602133
SELECT * FROM opportunities WHERE team_id = 1119 and remotely_created_at > '2026-02-01 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 1119 and updated_at > '2026-02-09 09:00:00' order by updated_at desc; # null
# [PASSWORD_DOTS]
select * from contacts where crm_provider_id = '003Uu00000ojD4NIAU';
select
cp.*
# DISTINCT t.id
# cp.id, cp.user_id, t.id, cp.crm_configuration_id, cp.contact_fields
FROM crm_profiles cp
JOIN crm_configurations crm on crm.id = cp.crm_configuration_id
JOIN users u on u.id = cp.user_id
JOIN teams t ON t.id = crm.team_id
WHERE crm.provider = 'salesforce' and t.status = 'active'
and cp.archived_at IS NULL and u.deleted_at IS NULL
and t.id NOT IN (1093)
and t.id = 2
and cp.contact_fields IS NULL;
# and c.crm_provider_id = '003Uu00000ojD4NIAU';
SELECT * FROM users WHERE id = 26484;
SELECT * FROM crm_profiles WHERE user_id = 26484;
SELECT * FROM social_accounts WHERE sociable_id = 26484;
SELECT * FROM crm_configurations where provider = 'salesforce';
select * from users where id IN (10022, 10403);
select * from users where team_id IN (526);
select * from teams where id IN (526, 532);
select * from crm_configurations where id IN (500, 516);
select * from crm_profiles where crm_configuration_id IN (500, 516) and user_id IN (10022, 10403);
select * from contacts where crm_configuration_id IN (500, 516) and crm_provider_id = '003Uu00000ojD4NIAU';
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 526 and sa.provider = 'salesforce';
select * from team_settings where team_id IN (526, 532);
select * from users where id IN (22824);
select * from crm_profiles where crm_configuration_id IN (1026);
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 1093 and sa.provider = 'salesforce';
select * from teams where id = 1099;
select * from users where id = 29643
select * from activity_processing_states;
SELECT * FROM teams where name LIKE '%Fare%'; # 233
SELECT * FROM opportunities where crm_configuration_id = 215
# and crm_provider_id = 'oppo_ogESZf2P50nDrd1nGPvKDXeA6sSaTN5v51Lp4ayVzKR'
;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 1088 and sa.provider = 'hubspot';
SELECT * FROM teams order by updated_at DESC
SELECT * FROM crm_configurations WHERE id = 1019; # SimpleConsign 1088 - no social account
select * from crm_configurations where provider = 'pipedrive';
select * from teams where id = 957;
select * from crm_configurations where id = 957;
SELECT * FROM teams WHERE name LIKE '%Prolific%'; # 544, 518, 10743
SELECT * FROM opportunities where crm_configuration_id = 518 order by id desc;
select * from users where team_id = 1; # 26726 - Gabriela Dureva
SELECT * FROM opportunities where user_id = 26726; # 16834447 - Prolific
select * from activities where user_id = 26726 order by id desc;
select * from contacts where crm_configuration_id = 1
and email IN ('[EMAIL]', '[EMAIL]'); # 2094416, 2093620
SELECT * FROM contacts WHERE id = 6284931;
SELECT p.* FROM activities a JOIN participants p ON a.id = p.activity_id
WHERE a.user_id = 26726 and p.lead_id IN (2094416, 2093620) and a.created_at > '2026-01-01 00:00:00' order by p.email;
select * from activities where id IN (75509259,75509261,75509261,75511034,75026464,75517602,75517605);
select * from crm_configurations where id = 1;
43801692-1aeb-32ce-acba-5b80a479701a
44c3c9cf-6f5e-75f3-8179-bc9f75dd2b1b
405975c0-b3d0-7aaa-821f-09d59cae6dd1
4caf848d-4bed-2299-b248-7788d41f9fca
49bedc3f-f196-eef3-89c3-dea6a3b4aa63
43420989-a09d-b8f8-9806-c8bbf7a02aac
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 1 and sa.provider = 'salesforce';
SELECT * FROM activities WHERE id = 75461988;
SELECT * FROM activities WHERE uuid_to_bin('d6c5052e-e972-49e9-8912-26f2f7d6c5f6') = uuid;
select * from contacts where id = 17900517;
select * from contact_roles cr join crm_configurations crm on cr.crm_configuration_id = crm.id
where crm.provider != 'salesforce';
select * from users where id = 21047;
SELECT * FROM crm_configurations WHERE id = 892;
SELECT * FROM teams WHERE id = 942;
select * from opportunities where team_id = 942 order by updated_at desc;
select * from contacts where team_id = 942 order by updated_at desc;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 942 and sa.provider = 'hubspot';
SELECT * FROM opportunities where team_id = 1 and crm_provider_id IN ('006Pq00000NeH6XIAV', '006Pq000007z8kdIAA'); # 10697889, 6621430
SELECT * FROM crm_configurations WHERE id = 1;
SELECT * FROM teams WHERE crm_id = 1;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 1 and sa.provider = 'salesforce';
select id, user_id, opportunity_fields from crm_profiles where crm_configuration_id = 1
SELECT * FROM opportunities where team_id = 1 order by updated_at desc; # 10697889, 6621430
select * from teams where id = 852;
select * from groups where id = 2286;
select * from sidekick_settings where team_id = 852;
select * from default_activity_types where team_id = 852;
SELECT cc.provider, cc.id, p.id, u.*
FROM users u
LEFT JOIN crm_profiles p ON u.id = p.user_id AND p.id IS NULL -- no profile
INNER JOIN teams t ON u.team_id = t.id AND t.status = 'active' -- team is active
INNER JOIN crm_configurations cc ON t.crm_id = cc.id
WHERE u.status = 1 AND u.deleted_at IS NULL
AND u.crm_required = 1
AND u.team_id = 1
ORDER BY u.team_id;
SELECT * FROM crm_profiles cp where cp.crm_configuration_id = 1 and cp.user_id IN (
18481
);
SELECT cc.provider, cc.id, p.id, u.*
FROM users u
LEFT JOIN crm_profiles p ON u.id = p.user_id
INNER JOIN teams t ON u.team_id = t.id AND t.status = 'active'
INNER JOIN crm_configurations cc ON t.crm_id = cc.id
WHERE u.status = 1
AND u.deleted_at IS NULL
AND u.crm_required = 1
# AND u.team_id = 1
AND p.id IS NULL -- Move this condition to WHERE clause
ORDER BY u.team_id;
SELECT * FROM opportunities WHERE id = 20002609;
select * from teams where id = 1122; # Velatir, 29953 - [EMAIL]
select * from crm_configurations where id = 1060;
select * from crm_layouts where crm_configuration_id = 1060;
SELECT * FROM crm_layout_entities WHERE crm_layout_id = 3596;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 1122 and sa.provider = 'hubspot';
select * from opportunities where team_id = 1122 order by updated_at desc;
select * from crm_field_data where object_type = 'contact';
SELECT * FROM activities WHERE uuid_to_bin('374fc8ed-3315-4c9f-9b25-318b7fd2928f') = uuid; # 76584262
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 248 and sa.provider = 'salesforce';
SELECT * FROM crm_profiles where user_id = 24115; # 005QF000002CswMYAS
SELECT * FROM users where id = 24115;
SELECT * FROM accounts where id = 4002896;
SELECT * FROM teams WHERE name LIKE '%adswerve%';
SELECT * FROM opportunities where crm_configuration_id = 230 AND crm_provider_id IN ("0069N000003GIQ9QAO","0061r000019yGP9AAM","0066900001S2KWlAAN","0066900001TDpj2AAD","0066900001b8uEwAAI","0069N000001rQi0QAE","006QF00000KD40mYAD","006QF00000LzpRJYAZ","0069N000002uomtQAA","0069N000002xlMLQAY","0066900001NV6ubAAD","0061r00001HJp45AAD","006QF00000uTlUoYAK","006QF00000v0bZqYAI");
SELECT * FROM opportunities WHERE crm_configuration_id = 230 AND crm_provider_id = '0069N000003GIQ9QAO'; # 6272203
SELECT u.id, u.email, ac.name, a.* FROM activities a
JOIN users u ON a.user_id = u.id
JOIN accounts ac ON a.account_id = ac.id
WHERE
uuid_to_bin('e3269598-b562-44fb-b5e9-9d2694dc63e0') = a.uuid or
uuid_to_bin('66ddc3ab-4e15-45aa-af0c-248c1eece593') = a.uuid or
uuid_to_bin('826bd328-e1cc-4213-b8d8-572454cacc07') = a.uuid;
select * from users where id = 5825;
SELECT * FROM activities WHERE uuid_to_bin('e56aa2e8-231a-421b-ab1f-cb38ed2bf573') = uuid;
select * from activities where uuid_to_bin('91e13b2f-2d1b-45f8-b1fd-1141b6563782') = uuid;
19594, 862
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 862 and sa.provider = 'salesforce';
select * from automated_reports where id = 36;
select ar.frequency, r.*, ar.* from automated_report_results r
join automated_reports ar on r.report_id = ar.id
where ar.frequency != 'one_off';
select s.* from activity_searches s join users u ON s.user_id = u.id where u.team_id = 882;
select * from nudges n where n.activity_search_id
select * from teams where created_at > '2026-03-09';
SELECT * FROM crm_layouts WHERE crm_configuration_id = 1065; # 1065
SELECT * FROM crm_layout_entities WHERE crm_layout_id = 3617;
select * from users where team_id = 1 and name like '%Lukas%'; # 7160
SELECT * FROM teams WHERE id = 575;
select * from opportunities where team_id = 575;
SELECT * FROM teams WHERE name LIKE '%Integrum ESG%'; # 1126, 1065,
select * from opportunities where team_id = 1126;
SELECT * FROM teams WHERE name LIKE '%Base%'; # 1125, 1063,
select * from opportunities where team_id = 1125;
select * from contacts c
where c.team_id = 882;
SELECT * FROM activities WHERE id = 76822967;
SELECT * FROM crm_profiles WHERE user_id = 15440;
SELECT * FROM crm_profiles WHERE crm_configuration_id = 555;
SELECT * FROM crm_configurations WHERE id = 555;
SELECT * FROM users WHERE id = 15440; # team. 581, gr. 15440, pl. 3911, act. field 162182
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 581 and sa.provider = 'salesforce';
SELECT * FROM automated_report_results order by id desc;
select * from features;
select * from team_features where feature_id = 40;
select * from teams where id = 556;
select * from automated_reports;
where id = 54; # 4fdd41f6-dcf0-30d0-b339-7345381b6044 , ["pdf","podcast"]
SELECT * FROM automated_report_results WHERE uuid_to_bin('822fa41b-afd3-43a9-a248-86b0e36f3131') = uuid;
select * from automated_report_results order by id desc;
SELECT * FROM automated_report_results WHERE id = 1919;
select * from automated_report_results WHERE report_id = 54;
select * from opportunities where id = 7594349;
SELECT * FROM teams WHERE name LIKE '%Les%'; # 711, 692, 16067 - [EMAIL]
select * from playbooks where team_id = 711; # event 226147
SELECT * FROM playbook_categories WHERE playbook_id = 5515;
SELECT * FROM crm_fields WHERE crm_configuration_id = 692 and object_type = 'event';
SELECT * FROM crm_fields WHERE id = 226147;
SELECT * FROM crm_field_values WHERE crm_field_id = 226147;
SELECT * FROM crm_configurations WHERE id = 692;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 711 and sa.provider = 'salesforce';
SELECT * FROM crm_profiles cp JOIN users u on u.id = cp.user_id WHERE u.team_id = 711;
select * from leads;
select * from calendars;
SELECT
t.id AS team_id,
t.name,
LOWER(SUBSTRING_INDEX(c.calendar_provider_id, '@', -1)) AS calendar_domain
FROM teams t
JOIN users u ON u.team_id = t.id
JOIN calendars c ON c.user_id = u.id AND c.status = 'active' AND c.calendar_provider_id LIKE '%@%'
LEFT JOIN team_domains td
ON td.team_id = t.id
AND td.deleted_at IS NULL
AND td.domain = LOWER(SUBSTRING_INDEX(c.calendar_provider_id, '@', -1))
GROUP BY t.id, t.name, calendar_domain
ORDER BY t.name, calendar_domain;
select * from users u join calendars c on c.user_id = u.id
where u.team_id = 882;
select * from activities where id = 74049485; # team 563 crm 537
select * from activities where id = 73272382; # team 563 crm 537
select * from activities where id = 64400389; # team 563 crm 537
select * from activities where id = 58081273; # team 563 crm 537
select * from activities where id = 54520297; # team 563 crm 537
select * from participants where activity_id = 58081273;
select * from activities where crm_configuration_id = 537 and provider = 'aircall'
and account_id = 19003658 order by updated_at desc;
select * from contacts where crm_configuration_id = 537 and id = 35957759;
select * from accounts where crm_configuration_id = 537 and id = 19003658;
select * from automated_report_results where id = 1976;
select * from automated_reports where id = 583;
select * from activity_searches where id = 87714;
select * from activity_search_filters where activity_search_id = 87714;
SELECT * FROM activities WHERE uuid_to_bin('8827f672-202d-4162-9d04-73ff5f0566a9') = uuid
or uuid_to_bin('47842446-af51-4bcb-854f-cc6560290101') = uuid;
SELECT * FROM crm_configurations WHERE provider = 'hubspot';
select * from rate_limits;
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<?php
declare(strict_types=1);
namespace Jiminny\Models;
use Illuminate\Database\Eloquent\Relations\MorphTo;
use Jiminny\Component\Eloquent\Builder;
use Jiminny\Contracts\Http\RateLimitInterface;
/**
* Jiminny\Models\RateLimit
*
* @property int $id
* @property \Illuminate\Support\Carbon|null $created_at
* @property \Illuminate\Support\Carbon|null $updated_at
* @property int $quota
* @property int $window
* @property int $limited_id
* @property string $limited_type
* @property-read \Illuminate\Database\Eloquent\Model $limited
*
* @method static Builder|RateLimit chunkByIdDesc($count, callable $callback, $column = null, $alias = null)
* @method static Builder|RateLimit newModelQuery()
* @method static Builder|RateLimit newQuery()
* @method static Builder|RateLimit query()
* @method static Builder|RateLimit whereCreatedAt($value)
* @method static Builder|RateLimit whereId($value)
* @method static Builder|RateLimit whereLimitedId($value)
* @method static Builder|RateLimit whereLimitedType($value)
* @method static Builder|RateLimit whereQuota($value)
* @method static Builder|RateLimit whereUpdatedAt($value)
* @method static Builder|RateLimit whereWindow($value)
*
* @mixin \Eloquent
*/
class RateLimit extends Model implements RateLimitInterface
{
protected $fillable = [
'quota',
'window',
];
public function limited(): MorphTo
{
return $this->morphTo();
}
public function getQuota(): int
{
return $this->attributes['quota'];
}
public function getWindow(): int
{
return $this->attributes['window'];
}
public function getKey(): string
{
$key = sprintf(
'%s.%s.%s.%s',
$this->limited_id,
$this->limited_type,
$this->getQuota(),
$this->getWindow()
);
return base64_encode($key);
}
}
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SELECT * FROM teams WHERE name LIKE '%litify%'; # 1069, 994, 24993
SELECT * FROM users WHERE id = 25061;
SELECT * FROM crm_profiles WHERE crm_configuration_id = 994;
SELECT * FROM crm_profiles WHERE user_id = 25061;
select * from crm_configurations where id = 834;
SELECT * FROM teams WHERE id = 882;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 882 and sa.provider = 'hubspot';
SELECT * FROM crm_configurations where id = 882; # 933 - GoGlobal
SELECT * FROM opportunities WHERE team_id = 933 order by updated_at desc;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 933 and sa.provider = 'hubspot';
SELECT * FROM crm_configurations WHERE provider = 'hubspot' and crm_provider_id = 7270388;
SELECT * FROM contacts where crm_configuration_id = 834;
SELECT * FROM opportunities WHERE team_id = 933
# AND crm_provider_id IN ('20131586060','46017317898','52543911090','53451356564','54101251892','54323768459');
AND id IN (8482561,18352941,19042734,19232139,19445140,19472541);
SELECT * FROM opportunity_contacts
WHERE opportunity_id IN (8482561,18352941,19042734,19232139,19445140,19472541);
# [PASSWORD_DOTS]
SELECT * FROM crm_configurations where id = 485; #
SELECT * FROM opportunities WHERE team_id = 933 order by updated_at desc;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 933 and sa.provider = 'hubspot';
select crm.provider, l.* from leads l join crm_configurations crm on l.crm_configuration_id = crm.id
where crm.provider NOT IN ('salesforce', 'integration-app', 'bullhorn', 'copper')
# and l.converted_at IS NOT NULL
;
# [PASSWORD_DOTS]
SELECT * FROM activities a WHERE type IN ('email-inbound', 'email-outbound')
and opportunity_id IS NULL
order by id desc;
SELECT * FROM teams WHERE id = 604; # 598
SELECT * FROM activities WHERE id = 74410828; # [EMAIL]
SELECT * FROM accounts WHERE id = 20068382;
SELECT * FROM accounts WHERE id = 35186038;
SELECT * FROM contacts WHERE team_id = 852 and updated_at > '2026-01-23 12:30:00' order by updated_at desc;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 559 and sa.provider = 'hubspot';
SELECT * FROM activities WHERE uuid_to_bin('cb6342b6-a183-401c-b0af-ede92b2ae763') = uuid;
select * from sidekick_settings where team_id = 781;
SELECT * FROM crm_configurations where provider = 'hubspot' and crm_provider_id = 26651871; # Teya
SELECT * FROM crm_configurations where provider = 'hubspot' and crm_provider_id = 7562435;
SELECT * FROM crm_configurations where provider = 'hubspot' and crm_provider_id = 8420347; # opflit 2100
SELECT * FROM crm_layouts WHERE crm_configuration_id = 711;
SELECT * FROM activities where crm_configuration_id = 711 and crm_provider_id IS NULL
and is_internal = 0 and status = 'completed'
order by id desc;
SELECT * FROM crm_layout_entities
WHERE crm_layout_id IN (2352, 2353);
;
SELECT * FROM crm_configurations where provider = 'hubspot' and id = 530;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 556 and sa.provider = 'hubspot';
SELECT * FROM activities WHERE uuid_to_bin('c6ca4b22-7738-4563-a95d-b8a9598924ae') = uuid;
SELECT * FROM activities WHERE uuid_to_bin('442abb2b-28bd-4be8-9c25-19e9bf02766d') = uuid;
select * from contacts
where crm_configuration_id = 530
and crm_provider_id = 872252;
select * from activities where crm_configuration_id = 530
and user_id = 14343 and type like '%softphone%'
and created_at between '2026-01-28 15:00:00' and '2026-01-28 15:10:00';
SELECT * FROM crm_configurations where provider = 'hubspot' and crm_provider_id = 25666868; # Teya
SELECT * FROM crm_configurations where provider = 'hubspot' and crm_provider_id = 8646335; # Teya
SELECT * FROM crm_configurations where provider = 'hubspot' and crm_provider_id IN (5933397);
SELECT t.name, t.id, t.owner_id, c.id, c.provider, c.crm_base_url FROM teams t
JOIN crm_configurations c ON t.id = c.team_id
WHERE t.status = 'active';
SELECT * FROM teams where id = 1091;
SELECT * FROM crm_configurations where team_id = 1091;
SELECT * FROM activity_providers where team_id = 1091;
SELECT * FROM activities where crm_configuration_id = 1024 and type IN ('softphone', 'softphone-outbound')
and provider NOT IN ('hubspot', 'aircall')
# and telephony_provider_id = '019c1131-a22f-4792-b9ea-20adf6a02ed0'
order by id desc;
SELECT * FROM teams WHERE name LIKE '%Leadventure%';
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 1091 and sa.provider = 'salesforce';
SELECT * FROM teams WHERE name LIKE '%Wilson%'; # 862, 812
SELECT * FROM teams where id = 862;
SELECT * FROM crm_configurations where team_id = 862;
SELECT * FROM activity_providers where team_id = 862;
SELECT * FROM activities where crm_configuration_id = 812 and type IN ('softphone', 'softphone-outbound')
and provider NOT IN ('hubspot', 'aircall')
# and telephony_provider_id = '019c1131-a22f-4792-b9ea-20adf6a02ed0'
order by id desc;
SELECT t.id, crm.id, crm.provider, ap.* FROM teams t
join crm_configurations crm on t.id = crm.team_id
join activity_providers ap on t.id = ap.team_id
where t.status = 'active' and ap.is_enabled = 1
and crm.provider = 'hubspot'
and ap.provider NOT IN ('hubspot', 'aircall', 'uploader', 'gong', 'twilio', 'zoom-bot', 'google-meet', 'ms-teams',
'outreach', 'close', 'ringcentral', 'dialpad', 'zoom-phone');
SELECT * FROM teams where id = 1068;
SELECT * FROM crm_configurations where team_id = 1068;
SELECT * FROM activity_providers where team_id = 1068;
SELECT * FROM activities a
where crm_configuration_id = 993 and type IN ('softphone', 'softphone-outbound')
and a.provider NOT IN ('hubspot', 'uploader', 'gong', 'twilio', 'google-meet', 'ms-teams','close'
)
# and telephony_provider_id = '019c1131-a22f-4792-b9ea-20adf6a02ed0'
order by a.id desc;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 1068 and sa.provider = 'hubspot';
# [PASSWORD_DOTS]
# [PASSWORD_DOTS]
# [PASSWORD_DOTS]
SELECT * FROM crm_configurations where id = 882; # 933 - GoGlobal , portalId: 6017093
SELECT * FROM opportunities WHERE team_id = 933 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 933 and updated_at > '2026-02-06 00:00:00' order by updated_at desc;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 933 and sa.provider = 'hubspot';
# [PASSWORD_DOTS]
SELECT * FROM crm_configurations where id = 834; # 882 - AnyVan , portalId: 5468262
SELECT * FROM contacts WHERE crm_configuration_id = 834 and updated_at > '2026-03-30 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE crm_configuration_id = 834 and updated_at > '2026-03-04 08:00:00' order by updated_at desc;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 882 and sa.provider = 'hubspot';
select * from crm_layouts where crm_configuration_id = 834;
select * from crm_layout_entities where crm_layout_id = 2780;
select * from crm_fields where id IN (321153,321192,321193,321194);
SELECT * FROM opportunities WHERE crm_configuration_id = 834 and id = 10993426;
# [PASSWORD_DOTS]
SELECT * FROM crm_configurations where id = 988; # 1057 - Teya (543ce4f4-168c-4571-91ea-5b35c253f06f) , portalId: 26651871
SELECT * FROM opportunities WHERE team_id = 1057 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 1057 and updated_at > '2026-02-04 00:00:00' order by updated_at desc;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 1057 and sa.provider = 'hubspot';
SELECT * FROM crm_configurations where id = 533; # 559 - Connectd , portalId: 6710988
SELECT * FROM opportunities WHERE team_id = 559 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 559 and updated_at > '2026-02-04 00:00:00' order by updated_at desc;
SELECT * FROM crm_configurations where id = 801; # 852 - Rise Vision , portalId: 2700250
SELECT * FROM opportunities WHERE team_id = 852 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 852 and updated_at > '2026-02-04 00:00:00' order by updated_at desc; # 6th last
SELECT * FROM crm_configurations where id = 962; # 1034 - evergrowth.io , portalId: 143180990
SELECT * FROM opportunities WHERE team_id = 1034 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 1034 and updated_at > '2026-02-04 00:00:00' order by updated_at desc;
# [PASSWORD_DOTS]
SELECT * FROM crm_configurations where id = 1037; # 1102 - Jibble , portalId: 6649755
SELECT * FROM opportunities WHERE team_id = 1102 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 1102 and updated_at > '2026-02-04 12:30:00' order by updated_at desc; # 8
SELECT * FROM crm_configurations where id = 1015; # 1049 - Travefy , portalId: 48904401
SELECT * FROM opportunities WHERE team_id = 1049 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 1049 and updated_at > '2026-02-04 12:30:00' order by updated_at desc; # 20
SELECT * FROM crm_configurations where id = 64; # 70 - SalaryFinance , portalId: 3404115
SELECT * FROM opportunities WHERE team_id = 70 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 70 and updated_at > '2026-02-04 12:30:00' order by updated_at desc; # 6th last
SELECT * FROM crm_configurations where id = 802; # 853 - Street Group , portalId: 7658438
SELECT * FROM opportunities WHERE team_id = 853 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 853 and updated_at > '2026-02-04 12:30:00' order by updated_at desc; # 10
SELECT * FROM crm_configurations where id = 872; # 921 - In Professional Development , portalId: 9238273
SELECT * FROM opportunities WHERE team_id = 921 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 921 and updated_at > '2026-02-04 12:30:00' order by updated_at desc; # 2
# [PASSWORD_DOTS]
SELECT * FROM crm_configurations where id = 550; # 576 - SeedLegals , portalId: 3028661
SELECT * FROM opportunities WHERE team_id = 576 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 576 and updated_at > '2026-02-05 14:00:00' order by updated_at desc;
SELECT * FROM crm_configurations where id = 989; # 1058 - rtaoutdoor.com , portalId: 22371204
SELECT * FROM opportunities WHERE team_id = 1058 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 1058 and updated_at > '2026-02-05 14:00:00' order by updated_at desc;
SELECT * FROM crm_configurations where id = 896; # 946 - Mintago , portalId: 6621281
SELECT * FROM opportunities WHERE team_id = 946 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 946 and updated_at > '2026-02-05 14:00:00' order by updated_at desc;
SELECT * FROM crm_configurations where id = 617; # 641 - PCS , portalId: 5244937
SELECT * FROM opportunities WHERE team_id = 641 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 641 and updated_at > '2026-02-05 14:00:00' order by updated_at desc; # 7th
# [PASSWORD_DOTS]
SELECT * FROM crm_configurations where id = 649; # 670 - Eventeny , portalId: 4492849
SELECT * FROM opportunities WHERE team_id = 670 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 670 and updated_at > '2026-02-09 08:00:00' order by updated_at desc; #
SELECT * FROM crm_configurations where id = 48; # 51 - CleanCloud , portalId: 4373137
SELECT * FROM opportunities WHERE team_id = 51 and updated_at > '2026-03-04 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 51 and updated_at > '2026-02-09 08:00:00' order by updated_at desc;
select * from users where team_id = 51; # 7783
SELECT * FROM groups WHERE uuid_to_bin('8a8d2cb6-8b55-4fa3-8b5c-5f0e3d8de59a') = uuid; # 1130
select * from activity_searches where user_id = 7783;
select * from activity_search_filters where activity_search_id IN (32291, 32292);
SELECT asf.activity_search_id, asf.id, asf.value
FROM activity_search_filters asf
WHERE asf.filter = 'group_id'
AND asf.value IN (
SELECT CONCAT(
HEX(SUBSTR(uuid, 5, 4)), '-',
HEX(SUBSTR(uuid, 3, 2)), '-',
HEX(SUBSTR(uuid, 1, 2)), '-',
HEX(SUBSTR(uuid, 9, 2)), '-',
HEX(SUBSTR(uuid, 11))
)
FROM groups
WHERE deleted_at IS NOT NULL
);
SELECT * FROM crm_configurations where id = 272; # 290 - Bonham & Brook , portalId: 5705856
SELECT * FROM opportunities WHERE team_id = 290 and updated_at > '2026-02-05 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 290 and updated_at > '2026-02-09 08:00:00' order by updated_at desc; # 6th
# [PASSWORD_DOTS]
SELECT * FROM crm_configurations where provider = 'hubspot';
SELECT * FROM crm_configurations where id = 1056; # 1119 - Chromatic , portalId: 45602133
SELECT * FROM opportunities WHERE team_id = 1119 and remotely_created_at > '2026-02-01 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 1119 and updated_at > '2026-02-09 09:00:00' order by updated_at desc; # null
# [PASSWORD_DOTS]
select * from contacts where crm_provider_id = '003Uu00000ojD4NIAU';
select
cp.*
# DISTINCT t.id
# cp.id, cp.user_id, t.id, cp.crm_configuration_id, cp.contact_fields
FROM crm_profiles cp
JOIN crm_configurations crm on crm.id = cp.crm_configuration_id
JOIN users u on u.id = cp.user_id
JOIN teams t ON t.id = crm.team_id
WHERE crm.provider = 'salesforce' and t.status = 'active'
and cp.archived_at IS NULL and u.deleted_at IS NULL
and t.id NOT IN (1093)
and t.id = 2
and cp.contact_fields IS NULL;
# and c.crm_provider_id = '003Uu00000ojD4NIAU';
SELECT * FROM users WHERE id = 26484;
SELECT * FROM crm_profiles WHERE user_id = 26484;
SELECT * FROM social_accounts WHERE sociable_id = 26484;
SELECT * FROM crm_configurations where provider = 'salesforce';
select * from users where id IN (10022, 10403);
select * from users where team_id IN (526);
select * from teams where id IN (526, 532);
select * from crm_configurations where id IN (500, 516);
select * from crm_profiles where crm_configuration_id IN (500, 516) and user_id IN (10022, 10403);
select * from contacts where crm_configuration_id IN (500, 516) and crm_provider_id = '003Uu00000ojD4NIAU';
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 526 and sa.provider = 'salesforce';
select * from team_settings where team_id IN (526, 532);
select * from users where id IN (22824);
select * from crm_profiles where crm_configuration_id IN (1026);
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 1093 and sa.provider = 'salesforce';
select * from teams where id = 1099;
select * from users where id = 29643
select * from activity_processing_states;
SELECT * FROM teams where name LIKE '%Fare%'; # 233
SELECT * FROM opportunities where crm_configuration_id = 215
# and crm_provider_id = 'oppo_ogESZf2P50nDrd1nGPvKDXeA6sSaTN5v51Lp4ayVzKR'
;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 1088 and sa.provider = 'hubspot';
SELECT * FROM teams order by updated_at DESC
SELECT * FROM crm_configurations WHERE id = 1019; # SimpleConsign 1088 - no social account
select * from crm_configurations where provider = 'pipedrive';
select * from teams where id = 957;
select * from crm_configurations where id = 957;
SELECT * FROM teams WHERE name LIKE '%Prolific%'; # 544, 518, 10743
SELECT * FROM opportunities where crm_configuration_id = 518 order by id desc;
select * from users where team_id = 1; # 26726 - Gabriela Dureva
SELECT * FROM opportunities where user_id = 26726; # 16834447 - Prolific
select * from activities where user_id = 26726 order by id desc;
select * from contacts where crm_configuration_id = 1
and email IN ('[EMAIL]', '[EMAIL]'); # 2094416, 2093620
SELECT * FROM contacts WHERE id = 6284931;
SELECT p.* FROM activities a JOIN participants p ON a.id = p.activity_id
WHERE a.user_id = 26726 and p.lead_id IN (2094416, 2093620) and a.created_at > '2026-01-01 00:00:00' order by p.email;
select * from activities where id IN (75509259,75509261,75509261,75511034,75026464,75517602,75517605);
select * from crm_configurations where id = 1;
43801692-1aeb-32ce-acba-5b80a479701a
44c3c9cf-6f5e-75f3-8179-bc9f75dd2b1b
405975c0-b3d0-7aaa-821f-09d59cae6dd1
4caf848d-4bed-2299-b248-7788d41f9fca
49bedc3f-f196-eef3-89c3-dea6a3b4aa63
43420989-a09d-b8f8-9806-c8bbf7a02aac
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 1 and sa.provider = 'salesforce';
SELECT * FROM activities WHERE id = 75461988;
SELECT * FROM activities WHERE uuid_to_bin('d6c5052e-e972-49e9-8912-26f2f7d6c5f6') = uuid;
select * from contacts where id = 17900517;
select * from contact_roles cr join crm_configurations crm on cr.crm_configuration_id = crm.id
where crm.provider != 'salesforce';
select * from users where id = 21047;
SELECT * FROM crm_configurations WHERE id = 892;
SELECT * FROM teams WHERE id = 942;
select * from opportunities where team_id = 942 order by updated_at desc;
select * from contacts where team_id = 942 order by updated_at desc;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 942 and sa.provider = 'hubspot';
SELECT * FROM opportunities where team_id = 1 and crm_provider_id IN ('006Pq00000NeH6XIAV', '006Pq000007z8kdIAA'); # 10697889, 6621430
SELECT * FROM crm_configurations WHERE id = 1;
SELECT * FROM teams WHERE crm_id = 1;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 1 and sa.provider = 'salesforce';
select id, user_id, opportunity_fields from crm_profiles where crm_configuration_id = 1
SELECT * FROM opportunities where team_id = 1 order by updated_at desc; # 10697889, 6621430
select * from teams where id = 852;
select * from groups where id = 2286;
select * from sidekick_settings where team_id = 852;
select * from default_activity_types where team_id = 852;
SELECT cc.provider, cc.id, p.id, u.*
FROM users u
LEFT JOIN crm_profiles p ON u.id = p.user_id AND p.id IS NULL -- no profile
INNER JOIN teams t ON u.team_id = t.id AND t.status = 'active' -- team is active
INNER JOIN crm_configurations cc ON t.crm_id = cc.id
WHERE u.status = 1 AND u.deleted_at IS NULL
AND u.crm_required = 1
AND u.team_id = 1
ORDER BY u.team_id;
SELECT * FROM crm_profiles cp where cp.crm_configuration_id = 1 and cp.user_id IN (
18481
);
SELECT cc.provider, cc.id, p.id, u.*
FROM users u
LEFT JOIN crm_profiles p ON u.id = p.user_id
INNER JOIN teams t ON u.team_id = t.id AND t.status = 'active'
INNER JOIN crm_configurations cc ON t.crm_id = cc.id
WHERE u.status = 1
AND u.deleted_at IS NULL
AND u.crm_required = 1
# AND u.team_id = 1
AND p.id IS NULL -- Move this condition to WHERE clause
ORDER BY u.team_id;
SELECT * FROM opportunities WHERE id = 20002609;
select * from teams where id = 1122; # Velatir, 29953 - [EMAIL]
select * from crm_configurations where id = 1060;
select * from crm_layouts where crm_configuration_id = 1060;
SELECT * FROM crm_layout_entities WHERE crm_layout_id = 3596;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 1122 and sa.provider = 'hubspot';
select * from opportunities where team_id = 1122 order by updated_at desc;
select * from crm_field_data where object_type = 'contact';
SELECT * FROM activities WHERE uuid_to_bin('374fc8ed-3315-4c9f-9b25-318b7fd2928f') = uuid; # 76584262
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 248 and sa.provider = 'salesforce';
SELECT * FROM crm_profiles where user_id = 24115; # 005QF000002CswMYAS
SELECT * FROM users where id = 24115;
SELECT * FROM accounts where id = 4002896;
SELECT * FROM teams WHERE name LIKE '%adswerve%';
SELECT * FROM opportunities where crm_configuration_id = 230 AND crm_provider_id IN ("0069N000003GIQ9QAO","0061r000019yGP9AAM","0066900001S2KWlAAN","0066900001TDpj2AAD","0066900001b8uEwAAI","0069N000001rQi0QAE","006QF00000KD40mYAD","006QF00000LzpRJYAZ","0069N000002uomtQAA","0069N000002xlMLQAY","0066900001NV6ubAAD","0061r00001HJp45AAD","006QF00000uTlUoYAK","006QF00000v0bZqYAI");
SELECT * FROM opportunities WHERE crm_configuration_id = 230 AND crm_provider_id = '0069N000003GIQ9QAO'; # 6272203
SELECT u.id, u.email, ac.name, a.* FROM activities a
JOIN users u ON a.user_id = u.id
JOIN accounts ac ON a.account_id = ac.id
WHERE
uuid_to_bin('e3269598-b562-44fb-b5e9-9d2694dc63e0') = a.uuid or
uuid_to_bin('66ddc3ab-4e15-45aa-af0c-248c1eece593') = a.uuid or
uuid_to_bin('826bd328-e1cc-4213-b8d8-572454cacc07') = a.uuid;
select * from users where id = 5825;
SELECT * FROM activities WHERE uuid_to_bin('e56aa2e8-231a-421b-ab1f-cb38ed2bf573') = uuid;
select * from activities where uuid_to_bin('91e13b2f-2d1b-45f8-b1fd-1141b6563782') = uuid;
19594, 862
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 862 and sa.provider = 'salesforce';
select * from automated_reports where id = 36;
select ar.frequency, r.*, ar.* from automated_report_results r
join automated_reports ar on r.report_id = ar.id
where ar.frequency != 'one_off';
select s.* from activity_searches s join users u ON s.user_id = u.id where u.team_id = 882;
select * from nudges n where n.activity_search_id
select * from teams where created_at > '2026-03-09';
SELECT * FROM crm_layouts WHERE crm_configuration_id = 1065; # 1065
SELECT * FROM crm_layout_entities WHERE crm_layout_id = 3617;
select * from users where team_id = 1 and name like '%Lukas%'; # 7160
SELECT * FROM teams WHERE id = 575;
select * from opportunities where team_id = 575;
SELECT * FROM teams WHERE name LIKE '%Integrum ESG%'; # 1126, 1065,
select * from opportunities where team_id = 1126;
SELECT * FROM teams WHERE name LIKE '%Base%'; # 1125, 1063,
select * from opportunities where team_id = 1125;
select * from contacts c
where c.team_id = 882;
SELECT * FROM activities WHERE id = 76822967;
SELECT * FROM crm_profiles WHERE user_id = 15440;
SELECT * FROM crm_profiles WHERE crm_configuration_id = 555;
SELECT * FROM crm_configurations WHERE id = 555;
SELECT * FROM users WHERE id = 15440; # team. 581, gr. 15440, pl. 3911, act. field 162182
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 581 and sa.provider = 'salesforce';
SELECT * FROM automated_report_results order by id desc;
select * from features;
select * from team_features where feature_id = 40;
select * from teams where id = 556;
select * from automated_reports;
where id = 54; # 4fdd41f6-dcf0-30d0-b339-7345381b6044 , ["pdf","podcast"]
SELECT * FROM automated_report_results WHERE uuid_to_bin('822fa41b-afd3-43a9-a248-86b0e36f3131') = uuid;
select * from automated_report_results order by id desc;
SELECT * FROM automated_report_results WHERE id = 1919;
select * from automated_report_results WHERE report_id = 54;
select * from opportunities where id = 7594349;
SELECT * FROM teams WHERE name LIKE '%Les%'; # 711, 692, 16067 - [EMAIL]
select * from playbooks where team_id = 711; # event 226147
SELECT * FROM playbook_categories WHERE playbook_id = 5515;
SELECT * FROM crm_fields WHERE crm_configuration_id = 692 and object_type = 'event';
SELECT * FROM crm_fields WHERE id = 226147;
SELECT * FROM crm_field_values WHERE crm_field_id = 226147;
SELECT * FROM crm_configurations WHERE id = 692;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 711 and sa.provider = 'salesforce';
SELECT * FROM crm_profiles cp JOIN users u on u.id = cp.user_id WHERE u.team_id = 711;
select * from leads;
select * from calendars;
SELECT
t.id AS team_id,
t.name,
LOWER(SUBSTRING_INDEX(c.calendar_provider_id, '@', -1)) AS calendar_domain
FROM teams t
JOIN users u ON u.team_id = t.id
JOIN calendars c ON c.user_id = u.id AND c.status = 'active' AND c.calendar_provider_id LIKE '%@%'
LEFT JOIN team_domains td
ON td.team_id = t.id
AND td.deleted_at IS NULL
AND td.domain = LOWER(SUBSTRING_INDEX(c.calendar_provider_id, '@', -1))
GROUP BY t.id, t.name, calendar_domain
ORDER BY t.name, calendar_domain;
select * from users u join calendars c on c.user_id = u.id
where u.team_id = 882;
select * from activities where id = 74049485; # team 563 crm 537
select * from activities where id = 73272382; # team 563 crm 537
select * from activities where id = 64400389; # team 563 crm 537
select * from activities where id = 58081273; # team 563 crm 537
select * from activities where id = 54520297; # team 563 crm 537
select * from participants where activity_id = 58081273;
select * from activities where crm_configuration_id = 537 and provider = 'aircall'
and account_id = 19003658 order by updated_at desc;
select * from contacts where crm_configuration_id = 537 and id = 35957759;
select * from accounts where crm_configuration_id = 537 and id = 19003658;
select * from automated_report_results where id = 1976;
select * from automated_reports where id = 583;
select * from activity_searches where id = 87714;
select * from activity_search_filters where activity_search_id = 87714;
SELECT * FROM activities WHERE uuid_to_bin('8827f672-202d-4162-9d04-73ff5f0566a9') = uuid
or uuid_to_bin('47842446-af51-4bcb-854f-cc6560290101') = uuid;
SELECT * FROM crm_configurations WHERE provider = 'hubspot';
select * from rate_limits;
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<?php
declare(strict_types=1);
namespace Jiminny\Models;
use Illuminate\Database\Eloquent\Relations\MorphTo;
use Jiminny\Component\Eloquent\Builder;
use Jiminny\Contracts\Http\RateLimitInterface;
/**
* Jiminny\Models\RateLimit
*
* @property int $id
* @property \Illuminate\Support\Carbon|null $created_at
* @property \Illuminate\Support\Carbon|null $updated_at
* @property int $quota
* @property int $window
* @property int $limited_id
* @property string $limited_type
* @property-read \Illuminate\Database\Eloquent\Model $limited
*
* @method static Builder|RateLimit chunkByIdDesc($count, callable $callback, $column = null, $alias = null)
* @method static Builder|RateLimit newModelQuery()
* @method static Builder|RateLimit newQuery()
* @method static Builder|RateLimit query()
* @method static Builder|RateLimit whereCreatedAt($value)
* @method static Builder|RateLimit whereId($value)
* @method static Builder|RateLimit whereLimitedId($value)
* @method static Builder|RateLimit whereLimitedType($value)
* @method static Builder|RateLimit whereQuota($value)
* @method static Builder|RateLimit whereUpdatedAt($value)
* @method static Builder|RateLimit whereWindow($value)
*
* @mixin \Eloquent
*/
class RateLimit extends Model implements RateLimitInterface
{
protected $fillable = [
'quota',
'window',
];
public function limited(): MorphTo
{
return $this->morphTo();
}
public function getQuota(): int
{
return $this->attributes['quota'];
}
public function getWindow(): int
{
return $this->attributes['window'];
}
public function getKey(): string
{
$key = sprintf(
'%s.%s.%s.%s',
$this->limited_id,
$this->limited_type,
$this->getQuota(),
$this->getWindow()
);
return base64_encode($key);
}
}
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SELECT * FROM teams WHERE name LIKE '%litify%'; # 1069, 994, 24993
SELECT * FROM users WHERE id = 25061;
SELECT * FROM crm_profiles WHERE crm_configuration_id = 994;
SELECT * FROM crm_profiles WHERE user_id = 25061;
select * from crm_configurations where id = 834;
SELECT * FROM teams WHERE id = 882;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 882 and sa.provider = 'hubspot';
SELECT * FROM crm_configurations where id = 882; # 933 - GoGlobal
SELECT * FROM opportunities WHERE team_id = 933 order by updated_at desc;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 933 and sa.provider = 'hubspot';
SELECT * FROM crm_configurations WHERE provider = 'hubspot' and crm_provider_id = 7270388;
SELECT * FROM contacts where crm_configuration_id = 834;
SELECT * FROM opportunities WHERE team_id = 933
# AND crm_provider_id IN ('20131586060','46017317898','52543911090','53451356564','54101251892','54323768459');
AND id IN (8482561,18352941,19042734,19232139,19445140,19472541);
SELECT * FROM opportunity_contacts
WHERE opportunity_id IN (8482561,18352941,19042734,19232139,19445140,19472541);
# [PASSWORD_DOTS]
SELECT * FROM crm_configurations where id = 485; #
SELECT * FROM opportunities WHERE team_id = 933 order by updated_at desc;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 933 and sa.provider = 'hubspot';
select crm.provider, l.* from leads l join crm_configurations crm on l.crm_configuration_id = crm.id
where crm.provider NOT IN ('salesforce', 'integration-app', 'bullhorn', 'copper')
# and l.converted_at IS NOT NULL
;
# [PASSWORD_DOTS]
SELECT * FROM activities a WHERE type IN ('email-inbound', 'email-outbound')
and opportunity_id IS NULL
order by id desc;
SELECT * FROM teams WHERE id = 604; # 598
SELECT * FROM activities WHERE id = 74410828; # [EMAIL]
SELECT * FROM accounts WHERE id = 20068382;
SELECT * FROM accounts WHERE id = 35186038;
SELECT * FROM contacts WHERE team_id = 852 and updated_at > '2026-01-23 12:30:00' order by updated_at desc;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 559 and sa.provider = 'hubspot';
SELECT * FROM activities WHERE uuid_to_bin('cb6342b6-a183-401c-b0af-ede92b2ae763') = uuid;
select * from sidekick_settings where team_id = 781;
SELECT * FROM crm_configurations where provider = 'hubspot' and crm_provider_id = 26651871; # Teya
SELECT * FROM crm_configurations where provider = 'hubspot' and crm_provider_id = 7562435;
SELECT * FROM crm_configurations where provider = 'hubspot' and crm_provider_id = 8420347; # opflit 2100
SELECT * FROM crm_layouts WHERE crm_configuration_id = 711;
SELECT * FROM activities where crm_configuration_id = 711 and crm_provider_id IS NULL
and is_internal = 0 and status = 'completed'
order by id desc;
SELECT * FROM crm_layout_entities
WHERE crm_layout_id IN (2352, 2353);
;
SELECT * FROM crm_configurations where provider = 'hubspot' and id = 530;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 556 and sa.provider = 'hubspot';
SELECT * FROM activities WHERE uuid_to_bin('c6ca4b22-7738-4563-a95d-b8a9598924ae') = uuid;
SELECT * FROM activities WHERE uuid_to_bin('442abb2b-28bd-4be8-9c25-19e9bf02766d') = uuid;
select * from contacts
where crm_configuration_id = 530
and crm_provider_id = 872252;
select * from activities where crm_configuration_id = 530
and user_id = 14343 and type like '%softphone%'
and created_at between '2026-01-28 15:00:00' and '2026-01-28 15:10:00';
SELECT * FROM crm_configurations where provider = 'hubspot' and crm_provider_id = 25666868; # Teya
SELECT * FROM crm_configurations where provider = 'hubspot' and crm_provider_id = 8646335; # Teya
SELECT * FROM crm_configurations where provider = 'hubspot' and crm_provider_id IN (5933397);
SELECT t.name, t.id, t.owner_id, c.id, c.provider, c.crm_base_url FROM teams t
JOIN crm_configurations c ON t.id = c.team_id
WHERE t.status = 'active';
SELECT * FROM teams where id = 1091;
SELECT * FROM crm_configurations where team_id = 1091;
SELECT * FROM activity_providers where team_id = 1091;
SELECT * FROM activities where crm_configuration_id = 1024 and type IN ('softphone', 'softphone-outbound')
and provider NOT IN ('hubspot', 'aircall')
# and telephony_provider_id = '019c1131-a22f-4792-b9ea-20adf6a02ed0'
order by id desc;
SELECT * FROM teams WHERE name LIKE '%Leadventure%';
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 1091 and sa.provider = 'salesforce';
SELECT * FROM teams WHERE name LIKE '%Wilson%'; # 862, 812
SELECT * FROM teams where id = 862;
SELECT * FROM crm_configurations where team_id = 862;
SELECT * FROM activity_providers where team_id = 862;
SELECT * FROM activities where crm_configuration_id = 812 and type IN ('softphone', 'softphone-outbound')
and provider NOT IN ('hubspot', 'aircall')
# and telephony_provider_id = '019c1131-a22f-4792-b9ea-20adf6a02ed0'
order by id desc;
SELECT t.id, crm.id, crm.provider, ap.* FROM teams t
join crm_configurations crm on t.id = crm.team_id
join activity_providers ap on t.id = ap.team_id
where t.status = 'active' and ap.is_enabled = 1
and crm.provider = 'hubspot'
and ap.provider NOT IN ('hubspot', 'aircall', 'uploader', 'gong', 'twilio', 'zoom-bot', 'google-meet', 'ms-teams',
'outreach', 'close', 'ringcentral', 'dialpad', 'zoom-phone');
SELECT * FROM teams where id = 1068;
SELECT * FROM crm_configurations where team_id = 1068;
SELECT * FROM activity_providers where team_id = 1068;
SELECT * FROM activities a
where crm_configuration_id = 993 and type IN ('softphone', 'softphone-outbound')
and a.provider NOT IN ('hubspot', 'uploader', 'gong', 'twilio', 'google-meet', 'ms-teams','close'
)
# and telephony_provider_id = '019c1131-a22f-4792-b9ea-20adf6a02ed0'
order by a.id desc;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 1068 and sa.provider = 'hubspot';
# [PASSWORD_DOTS]
# [PASSWORD_DOTS]
# [PASSWORD_DOTS]
SELECT * FROM crm_configurations where id = 882; # 933 - GoGlobal , portalId: 6017093
SELECT * FROM opportunities WHERE team_id = 933 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 933 and updated_at > '2026-02-06 00:00:00' order by updated_at desc;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 933 and sa.provider = 'hubspot';
# [PASSWORD_DOTS]
SELECT * FROM crm_configurations where id = 834; # 882 - AnyVan , portalId: 5468262
SELECT * FROM contacts WHERE crm_configuration_id = 834 and updated_at > '2026-03-30 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE crm_configuration_id = 834 and updated_at > '2026-03-04 08:00:00' order by updated_at desc;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 882 and sa.provider = 'hubspot';
select * from crm_layouts where crm_configuration_id = 834;
select * from crm_layout_entities where crm_layout_id = 2780;
select * from crm_fields where id IN (321153,321192,321193,321194);
SELECT * FROM opportunities WHERE crm_configuration_id = 834 and id = 10993426;
# [PASSWORD_DOTS]
SELECT * FROM crm_configurations where id = 988; # 1057 - Teya (543ce4f4-168c-4571-91ea-5b35c253f06f) , portalId: 26651871
SELECT * FROM opportunities WHERE team_id = 1057 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 1057 and updated_at > '2026-02-04 00:00:00' order by updated_at desc;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 1057 and sa.provider = 'hubspot';
SELECT * FROM crm_configurations where id = 533; # 559 - Connectd , portalId: 6710988
SELECT * FROM opportunities WHERE team_id = 559 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 559 and updated_at > '2026-02-04 00:00:00' order by updated_at desc;
SELECT * FROM crm_configurations where id = 801; # 852 - Rise Vision , portalId: 2700250
SELECT * FROM opportunities WHERE team_id = 852 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 852 and updated_at > '2026-02-04 00:00:00' order by updated_at desc; # 6th last
SELECT * FROM crm_configurations where id = 962; # 1034 - evergrowth.io , portalId: 143180990
SELECT * FROM opportunities WHERE team_id = 1034 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 1034 and updated_at > '2026-02-04 00:00:00' order by updated_at desc;
# [PASSWORD_DOTS]
SELECT * FROM crm_configurations where id = 1037; # 1102 - Jibble , portalId: 6649755
SELECT * FROM opportunities WHERE team_id = 1102 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 1102 and updated_at > '2026-02-04 12:30:00' order by updated_at desc; # 8
SELECT * FROM crm_configurations where id = 1015; # 1049 - Travefy , portalId: 48904401
SELECT * FROM opportunities WHERE team_id = 1049 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 1049 and updated_at > '2026-02-04 12:30:00' order by updated_at desc; # 20
SELECT * FROM crm_configurations where id = 64; # 70 - SalaryFinance , portalId: 3404115
SELECT * FROM opportunities WHERE team_id = 70 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 70 and updated_at > '2026-02-04 12:30:00' order by updated_at desc; # 6th last
SELECT * FROM crm_configurations where id = 802; # 853 - Street Group , portalId: 7658438
SELECT * FROM opportunities WHERE team_id = 853 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 853 and updated_at > '2026-02-04 12:30:00' order by updated_at desc; # 10
SELECT * FROM crm_configurations where id = 872; # 921 - In Professional Development , portalId: 9238273
SELECT * FROM opportunities WHERE team_id = 921 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 921 and updated_at > '2026-02-04 12:30:00' order by updated_at desc; # 2
# [PASSWORD_DOTS]
SELECT * FROM crm_configurations where id = 550; # 576 - SeedLegals , portalId: 3028661
SELECT * FROM opportunities WHERE team_id = 576 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 576 and updated_at > '2026-02-05 14:00:00' order by updated_at desc;
SELECT * FROM crm_configurations where id = 989; # 1058 - rtaoutdoor.com , portalId: 22371204
SELECT * FROM opportunities WHERE team_id = 1058 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 1058 and updated_at > '2026-02-05 14:00:00' order by updated_at desc;
SELECT * FROM crm_configurations where id = 896; # 946 - Mintago , portalId: 6621281
SELECT * FROM opportunities WHERE team_id = 946 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 946 and updated_at > '2026-02-05 14:00:00' order by updated_at desc;
SELECT * FROM crm_configurations where id = 617; # 641 - PCS , portalId: 5244937
SELECT * FROM opportunities WHERE team_id = 641 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 641 and updated_at > '2026-02-05 14:00:00' order by updated_at desc; # 7th
# [PASSWORD_DOTS]
SELECT * FROM crm_configurations where id = 649; # 670 - Eventeny , portalId: 4492849
SELECT * FROM opportunities WHERE team_id = 670 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 670 and updated_at > '2026-02-09 08:00:00' order by updated_at desc; #
SELECT * FROM crm_configurations where id = 48; # 51 - CleanCloud , portalId: 4373137
SELECT * FROM opportunities WHERE team_id = 51 and updated_at > '2026-03-04 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 51 and updated_at > '2026-02-09 08:00:00' order by updated_at desc;
select * from users where team_id = 51; # 7783
SELECT * FROM groups WHERE uuid_to_bin('8a8d2cb6-8b55-4fa3-8b5c-5f0e3d8de59a') = uuid; # 1130
select * from activity_searches where user_id = 7783;
select * from activity_search_filters where activity_search_id IN (32291, 32292);
SELECT asf.activity_search_id, asf.id, asf.value
FROM activity_search_filters asf
WHERE asf.filter = 'group_id'
AND asf.value IN (
SELECT CONCAT(
HEX(SUBSTR(uuid, 5, 4)), '-',
HEX(SUBSTR(uuid, 3, 2)), '-',
HEX(SUBSTR(uuid, 1, 2)), '-',
HEX(SUBSTR(uuid, 9, 2)), '-',
HEX(SUBSTR(uuid, 11))
)
FROM groups
WHERE deleted_at IS NOT NULL
);
SELECT * FROM crm_configurations where id = 272; # 290 - Bonham & Brook , portalId: 5705856
SELECT * FROM opportunities WHERE team_id = 290 and updated_at > '2026-02-05 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 290 and updated_at > '2026-02-09 08:00:00' order by updated_at desc; # 6th
# [PASSWORD_DOTS]
SELECT * FROM crm_configurations where provider = 'hubspot';
SELECT * FROM crm_configurations where id = 1056; # 1119 - Chromatic , portalId: 45602133
SELECT * FROM opportunities WHERE team_id = 1119 and remotely_created_at > '2026-02-01 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 1119 and updated_at > '2026-02-09 09:00:00' order by updated_at desc; # null
# [PASSWORD_DOTS]
select * from contacts where crm_provider_id = '003Uu00000ojD4NIAU';
select
cp.*
# DISTINCT t.id
# cp.id, cp.user_id, t.id, cp.crm_configuration_id, cp.contact_fields
FROM crm_profiles cp
JOIN crm_configurations crm on crm.id = cp.crm_configuration_id
JOIN users u on u.id = cp.user_id
JOIN teams t ON t.id = crm.team_id
WHERE crm.provider = 'salesforce' and t.status = 'active'
and cp.archived_at IS NULL and u.deleted_at IS NULL
and t.id NOT IN (1093)
and t.id = 2
and cp.contact_fields IS NULL;
# and c.crm_provider_id = '003Uu00000ojD4NIAU';
SELECT * FROM users WHERE id = 26484;
SELECT * FROM crm_profiles WHERE user_id = 26484;
SELECT * FROM social_accounts WHERE sociable_id = 26484;
SELECT * FROM crm_configurations where provider = 'salesforce';
select * from users where id IN (10022, 10403);
select * from users where team_id IN (526);
select * from teams where id IN (526, 532);
select * from crm_configurations where id IN (500, 516);
select * from crm_profiles where crm_configuration_id IN (500, 516) and user_id IN (10022, 10403);
select * from contacts where crm_configuration_id IN (500, 516) and crm_provider_id = '003Uu00000ojD4NIAU';
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 526 and sa.provider = 'salesforce';
select * from team_settings where team_id IN (526, 532);
select * from users where id IN (22824);
select * from crm_profiles where crm_configuration_id IN (1026);
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 1093 and sa.provider = 'salesforce';
select * from teams where id = 1099;
select * from users where id = 29643
select * from activity_processing_states;
SELECT * FROM teams where name LIKE '%Fare%'; # 233
SELECT * FROM opportunities where crm_configuration_id = 215
# and crm_provider_id = 'oppo_ogESZf2P50nDrd1nGPvKDXeA6sSaTN5v51Lp4ayVzKR'
;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 1088 and sa.provider = 'hubspot';
SELECT * FROM teams order by updated_at DESC
SELECT * FROM crm_configurations WHERE id = 1019; # SimpleConsign 1088 - no social account
select * from crm_configurations where provider = 'pipedrive';
select * from teams where id = 957;
select * from crm_configurations where id = 957;
SELECT * FROM teams WHERE name LIKE '%Prolific%'; # 544, 518, 10743
SELECT * FROM opportunities where crm_configuration_id = 518 order by id desc;
select * from users where team_id = 1; # 26726 - Gabriela Dureva
SELECT * FROM opportunities where user_id = 26726; # 16834447 - Prolific
select * from activities where user_id = 26726 order by id desc;
select * from contacts where crm_configuration_id = 1
and email IN ('[EMAIL]', '[EMAIL]'); # 2094416, 2093620
SELECT * FROM contacts WHERE id = 6284931;
SELECT p.* FROM activities a JOIN participants p ON a.id = p.activity_id
WHERE a.user_id = 26726 and p.lead_id IN (2094416, 2093620) and a.created_at > '2026-01-01 00:00:00' order by p.email;
select * from activities where id IN (75509259,75509261,75509261,75511034,75026464,75517602,75517605);
select * from crm_configurations where id = 1;
43801692-1aeb-32ce-acba-5b80a479701a
44c3c9cf-6f5e-75f3-8179-bc9f75dd2b1b
405975c0-b3d0-7aaa-821f-09d59cae6dd1
4caf848d-4bed-2299-b248-7788d41f9fca
49bedc3f-f196-eef3-89c3-dea6a3b4aa63
43420989-a09d-b8f8-9806-c8bbf7a02aac
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 1 and sa.provider = 'salesforce';
SELECT * FROM activities WHERE id = 75461988;
SELECT * FROM activities WHERE uuid_to_bin('d6c5052e-e972-49e9-8912-26f2f7d6c5f6') = uuid;
select * from contacts where id = 17900517;
select * from contact_roles cr join crm_configurations crm on cr.crm_configuration_id = crm.id
where crm.provider != 'salesforce';
select * from users where id = 21047;
SELECT * FROM crm_configurations WHERE id = 892;
SELECT * FROM teams WHERE id = 942;
select * from opportunities where team_id = 942 order by updated_at desc;
select * from contacts where team_id = 942 order by updated_at desc;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 942 and sa.provider = 'hubspot';
SELECT * FROM opportunities where team_id = 1 and crm_provider_id IN ('006Pq00000NeH6XIAV', '006Pq000007z8kdIAA'); # 10697889, 6621430
SELECT * FROM crm_configurations WHERE id = 1;
SELECT * FROM teams WHERE crm_id = 1;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 1 and sa.provider = 'salesforce';
select id, user_id, opportunity_fields from crm_profiles where crm_configuration_id = 1
SELECT * FROM opportunities where team_id = 1 order by updated_at desc; # 10697889, 6621430
select * from teams where id = 852;
select * from groups where id = 2286;
select * from sidekick_settings where team_id = 852;
select * from default_activity_types where team_id = 852;
SELECT cc.provider, cc.id, p.id, u.*
FROM users u
LEFT JOIN crm_profiles p ON u.id = p.user_id AND p.id IS NULL -- no profile
INNER JOIN teams t ON u.team_id = t.id AND t.status = 'active' -- team is active
INNER JOIN crm_configurations cc ON t.crm_id = cc.id
WHERE u.status = 1 AND u.deleted_at IS NULL
AND u.crm_required = 1
AND u.team_id = 1
ORDER BY u.team_id;
SELECT * FROM crm_profiles cp where cp.crm_configuration_id = 1 and cp.user_id IN (
18481
);
SELECT cc.provider, cc.id, p.id, u.*
FROM users u
LEFT JOIN crm_profiles p ON u.id = p.user_id
INNER JOIN teams t ON u.team_id = t.id AND t.status = 'active'
INNER JOIN crm_configurations cc ON t.crm_id = cc.id
WHERE u.status = 1
AND u.deleted_at IS NULL
AND u.crm_required = 1
# AND u.team_id = 1
AND p.id IS NULL -- Move this condition to WHERE clause
ORDER BY u.team_id;
SELECT * FROM opportunities WHERE id = 20002609;
select * from teams where id = 1122; # Velatir, 29953 - [EMAIL]
select * from crm_configurations where id = 1060;
select * from crm_layouts where crm_configuration_id = 1060;
SELECT * FROM crm_layout_entities WHERE crm_layout_id = 3596;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 1122 and sa.provider = 'hubspot';
select * from opportunities where team_id = 1122 order by updated_at desc;
select * from crm_field_data where object_type = 'contact';
SELECT * FROM activities WHERE uuid_to_bin('374fc8ed-3315-4c9f-9b25-318b7fd2928f') = uuid; # 76584262
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 248 and sa.provider = 'salesforce';
SELECT * FROM crm_profiles where user_id = 24115; # 005QF000002CswMYAS
SELECT * FROM users where id = 24115;
SELECT * FROM accounts where id = 4002896;
SELECT * FROM teams WHERE name LIKE '%adswerve%';
SELECT * FROM opportunities where crm_configuration_id = 230 AND crm_provider_id IN ("0069N000003GIQ9QAO","0061r000019yGP9AAM","0066900001S2KWlAAN","0066900001TDpj2AAD","0066900001b8uEwAAI","0069N000001rQi0QAE","006QF00000KD40mYAD","006QF00000LzpRJYAZ","0069N000002uomtQAA","0069N000002xlMLQAY","0066900001NV6ubAAD","0061r00001HJp45AAD","006QF00000uTlUoYAK","006QF00000v0bZqYAI");
SELECT * FROM opportunities WHERE crm_configuration_id = 230 AND crm_provider_id = '0069N000003GIQ9QAO'; # 6272203
SELECT u.id, u.email, ac.name, a.* FROM activities a
JOIN users u ON a.user_id = u.id
JOIN accounts ac ON a.account_id = ac.id
WHERE
uuid_to_bin('e3269598-b562-44fb-b5e9-9d2694dc63e0') = a.uuid or
uuid_to_bin('66ddc3ab-4e15-45aa-af0c-248c1eece593') = a.uuid or
uuid_to_bin('826bd328-e1cc-4213-b8d8-572454cacc07') = a.uuid;
select * from users where id = 5825;
SELECT * FROM activities WHERE uuid_to_bin('e56aa2e8-231a-421b-ab1f-cb38ed2bf573') = uuid;
select * from activities where uuid_to_bin('91e13b2f-2d1b-45f8-b1fd-1141b6563782') = uuid;
19594, 862
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 862 and sa.provider = 'salesforce';
select * from automated_reports where id = 36;
select ar.frequency, r.*, ar.* from automated_report_results r
join automated_reports ar on r.report_id = ar.id
where ar.frequency != 'one_off';
select s.* from activity_searches s join users u ON s.user_id = u.id where u.team_id = 882;
select * from nudges n where n.activity_search_id
select * from teams where created_at > '2026-03-09';
SELECT * FROM crm_layouts WHERE crm_configuration_id = 1065; # 1065
SELECT * FROM crm_layout_entities WHERE crm_layout_id = 3617;
select * from users where team_id = 1 and name like '%Lukas%'; # 7160
SELECT * FROM teams WHERE id = 575;
select * from opportunities where team_id = 575;
SELECT * FROM teams WHERE name LIKE '%Integrum ESG%'; # 1126, 1065,
select * from opportunities where team_id = 1126;
SELECT * FROM teams WHERE name LIKE '%Base%'; # 1125, 1063,
select * from opportunities where team_id = 1125;
select * from contacts c
where c.team_id = 882;
SELECT * FROM activities WHERE id = 76822967;
SELECT * FROM crm_profiles WHERE user_id = 15440;
SELECT * FROM crm_profiles WHERE crm_configuration_id = 555;
SELECT * FROM crm_configurations WHERE id = 555;
SELECT * FROM users WHERE id = 15440; # team. 581, gr. 15440, pl. 3911, act. field 162182
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 581 and sa.provider = 'salesforce';
SELECT * FROM automated_report_results order by id desc;
select * from features;
select * from team_features where feature_id = 40;
select * from teams where id = 556;
select * from automated_reports;
where id = 54; # 4fdd41f6-dcf0-30d0-b339-7345381b6044 , ["pdf","podcast"]
SELECT * FROM automated_report_results WHERE uuid_to_bin('822fa41b-afd3-43a9-a248-86b0e36f3131') = uuid;
select * from automated_report_results order by id desc;
SELECT * FROM automated_report_results WHERE id = 1919;
select * from automated_report_results WHERE report_id = 54;
select * from opportunities where id = 7594349;
SELECT * FROM teams WHERE name LIKE '%Les%'; # 711, 692, 16067 - [EMAIL]
select * from playbooks where team_id = 711; # event 226147
SELECT * FROM playbook_categories WHERE playbook_id = 5515;
SELECT * FROM crm_fields WHERE crm_configuration_id = 692 and object_type = 'event';
SELECT * FROM crm_fields WHERE id = 226147;
SELECT * FROM crm_field_values WHERE crm_field_id = 226147;
SELECT * FROM crm_configurations WHERE id = 692;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 711 and sa.provider = 'salesforce';
SELECT * FROM crm_profiles cp JOIN users u on u.id = cp.user_id WHERE u.team_id = 711;
select * from leads;
select * from calendars;
SELECT
t.id AS team_id,
t.name,
LOWER(SUBSTRING_INDEX(c.calendar_provider_id, '@', -1)) AS calendar_domain
FROM teams t
JOIN users u ON u.team_id = t.id
JOIN calendars c ON c.user_id = u.id AND c.status = 'active' AND c.calendar_provider_id LIKE '%@%'
LEFT JOIN team_domains td
ON td.team_id = t.id
AND td.deleted_at IS NULL
AND td.domain = LOWER(SUBSTRING_INDEX(c.calendar_provider_id, '@', -1))
GROUP BY t.id, t.name, calendar_domain
ORDER BY t.name, calendar_domain;
select * from users u join calendars c on c.user_id = u.id
where u.team_id = 882;
select * from activities where id = 74049485; # team 563 crm 537
select * from activities where id = 73272382; # team 563 crm 537
select * from activities where id = 64400389; # team 563 crm 537
select * from activities where id = 58081273; # team 563 crm 537
select * from activities where id = 54520297; # team 563 crm 537
select * from participants where activity_id = 58081273;
select * from activities where crm_configuration_id = 537 and provider = 'aircall'
and account_id = 19003658 order by updated_at desc;
select * from contacts where crm_configuration_id = 537 and id = 35957759;
select * from accounts where crm_configuration_id = 537 and id = 19003658;
select * from automated_report_results where id = 1976;
select * from automated_reports where id = 583;
select * from activity_searches where id = 87714;
select * from activity_search_filters where activity_search_id = 87714;
SELECT * FROM activities WHERE uuid_to_bin('8827f672-202d-4162-9d04-73ff5f0566a9') = uuid
or uuid_to_bin('47842446-af51-4bcb-854f-cc6560290101') = uuid;
SELECT * FROM crm_configurations WHERE provider = 'hubspot';
select * from rate_limits;
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<?php
declare(strict_types=1);
namespace Jiminny\Models;
use Illuminate\Database\Eloquent\Relations\MorphTo;
use Jiminny\Component\Eloquent\Builder;
use Jiminny\Contracts\Http\RateLimitInterface;
/**
* Jiminny\Models\RateLimit
*
* @property int $id
* @property \Illuminate\Support\Carbon|null $created_at
* @property \Illuminate\Support\Carbon|null $updated_at
* @property int $quota
* @property int $window
* @property int $limited_id
* @property string $limited_type
* @property-read \Illuminate\Database\Eloquent\Model $limited
*
* @method static Builder|RateLimit chunkByIdDesc($count, callable $callback, $column = null, $alias = null)
* @method static Builder|RateLimit newModelQuery()
* @method static Builder|RateLimit newQuery()
* @method static Builder|RateLimit query()
* @method static Builder|RateLimit whereCreatedAt($value)
* @method static Builder|RateLimit whereId($value)
* @method static Builder|RateLimit whereLimitedId($value)
* @method static Builder|RateLimit whereLimitedType($value)
* @method static Builder|RateLimit whereQuota($value)
* @method static Builder|RateLimit whereUpdatedAt($value)
* @method static Builder|RateLimit whereWindow($value)
*
* @mixin \Eloquent
*/
class RateLimit extends Model implements RateLimitInterface
{
protected $fillable = [
'quota',
'window',
];
public function limited(): MorphTo
{
return $this->morphTo();
}
public function getQuota(): int
{
return $this->attributes['quota'];
}
public function getWindow(): int
{
return $this->attributes['window'];
}
public function getKey(): string
{
$key = sprintf(
'%s.%s.%s.%s',
$this->limited_id,
$this->limited_type,
$this->getQuota(),
$this->getWindow()
);
return base64_encode($key);
}
}
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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
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10:42
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Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
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You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
Retry
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Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
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php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.
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You said: Ok, so what will happen each time I make a request?
You said: Ok, so what will happen each time I make a request?
Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?
13:44
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Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Analyzed per-request overhead concerns and quantified Redis performance impact
Analyzed per-request overhead concerns and quantified Redis performance impact
Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out. But the overhead is much smaller than it sounds, and importantly, it's nowhere near your bottleneck.
What "two buckets per request" actually costs
What "two buckets per request" actually costs
Each request makes
one round trip to Redis
— not two — because you fold both bucket checks into a single Lua script that runs atomically server-side:
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lua
-- KEYS[1] = burst bucket key (or search)
-- KEYS[2] = daily bucket key
-- ARGV[1] = now (ms) ARGV[2] = window_ms
-- ARGV[3] = bucket_max ARGV[4] = daily_max
-- ARGV[5] = request_id ARGV[6] = daily_ttl
-- Trim sliding window
redis
.
call
(
'ZREMRANGEBYSCORE'
,
KEYS
[
1
]
,
0
,
ARGV
[
1
]
-
ARGV
[
2
]
)
local
burst_used
=
redis
.
call
(
'ZCARD'
,
KEYS
[
1
]
)
local
daily_used
=
tonumber
(
redis
.
call
(
'GET'
,
KEYS
[
2
]
)
or
'0'
)
if
burst_used
>=
tonumber
(
ARGV
[
3
]
)
then
-- Tell caller how long to sleep until oldest entry expires
local
oldest
=
redis
.
call
(
'ZRANGE'
,
KEYS
[
1
]
,
0
,
0
,
'WITHSCORES'
)
return
{
0
,
'BURST'
,
(
oldest
[
2
]
+
ARGV
[
2
]
)
-
ARGV
[
1
]
}
end
if
daily_used
>=
tonumber
(
ARGV
[
4
]
)
then
return
{
0
,
'DAILY'
,
-
1
}
end
redis
.
call
(
'ZADD'
,
KEYS...
|
Claude
|
Claude
|
NULL
|
1963
|
|
1962
|
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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
Show more
10:42
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Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
Copy
Give positive feedback
Give negative feedback
Retry
You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
Retry
Edit
Copy
Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
Copy to clipboard
php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.
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Retry
You said: Ok, so what will happen each time I make a request?
You said: Ok, so what will happen each time I make a request?
Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?
13:44
Retry
Edit
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Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Analyzed per-request overhead concerns and quantified Redis performance impact
Analyzed per-request overhead concerns and quantified Redis performance impact
Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out. But the overhead is much smaller than it sounds, and importantly, it's nowhere near your bottleneck.
What "two buckets per request" actually costs
What "two buckets per request" actually costs
Each request makes
one round trip to Redis
— not two — because you fold both bucket checks into a single Lua script that runs atomically server-side:
Copy to clipboard
lua
-- KEYS[1] = burst bucket key (or search)
-- KEYS[2] = daily bucket key
-- ARGV[1] = now (ms) ARGV[2] = window_ms
-- ARGV[3] = bucket_max ARGV[4] = daily_max
-- ARGV[5] = request_id ARGV[6] = daily_ttl
-- Trim sliding window
redis
.
call
(
'ZREMRANGEBYSCORE'
,
KEYS
[
1
]
,
0
,
ARGV
[
1
]
-
ARGV
[
2
]
)
local
burst_used
=
redis
.
call
(
'ZCARD'
,
KEYS
[
1
]
)
local
daily_used
=
tonumber
(
redis
.
call
(
'GET'
,
KEYS
[
2
]
)
or
'0'
)
if
burst_used
>=
tonumber
(
ARGV
[
3
]
)
then
-- Tell caller how long to sleep until oldest entry expires
local
oldest
=
redis
.
call
(
'ZRANGE'
,
KEYS
[
1
]
,
0
,
0
,
'WITHSCORES'
)
return
{
0
,
'BURST'
,
(
oldest
[
2
]
+
ARGV
[
2
]
)
-
ARGV
[
1
]
}
end
if
daily_used
>=
tonumber
(
ARGV
[
4
]
)
then
return
{
0
,
'DAILY'
,
-
1
}
end
redis
.
call
(
'ZADD'
,
KEYS
[
1
]
,
ARGV
[
1
]
,
ARGV
[
5
]
)
redis
.
call
(
'PEXPIRE'
,
KEYS
[
1
]
,
ARGV
[
2
]
+
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...
|
Claude
|
Claude
|
NULL
|
1962
|
|
1961
|
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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.
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Give positive feedback
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Retry
You said: Ok, so what will happen each time I make a request?
You said: Ok, so what will happen each time I make a request?
Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?
13:44
Retry
Edit
Copy
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Analyzed per-request overhead concerns and quantified Redis performance impact
Analyzed per-request overhead concerns and quantified Redis performance impact
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
(...
|
Claude
|
Claude
|
NULL
|
1961
|
|
1960
|
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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
Show more
10:42
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Copy
Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
Copy
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Retry
You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
Retry
Edit
Copy
Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
Copy to clipboard
php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.
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Retry
You said: Ok, so what will happen each time I make a request?
You said: Ok, so what will happen each time I make a request?
Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?
13:44
Retry
Edit
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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
]...
|
Claude
|
Claude
|
NULL
|
1960
|
|
1959
|
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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
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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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Retry
You said: Ok, so what will happen each time I make a request?
You said: Ok, so what will happen each time I make a request?
Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?
13:44
Retry
Edit
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Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Analyzed per-request overhead concerns and quantified Redis performance impact
Analyzed per-request overhead concerns and quantified Redis performance impact
Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out. But the overhead is much smaller than it sounds, and importantly, it's nowhere near your bottleneck.
What "two buckets per request" actually costs
What "two buckets per request" actually costs
Each request makes
one round trip to Redis
— not two — because you fold both bucket checks into a single Lua script that runs atomically server-side:
Copy to clipboard
lua
-- KEYS[1] = burst bucket key (or search)
-- KEYS[2] = daily bucket key
-- ARGV[1] = now (ms) ARGV[2] = window_ms
-- ARGV[3] = bucket_max ARGV[4] = daily_max
-- ARGV[5] = request_id ARGV[6] = daily_ttl
-- Trim sliding window
redis
.
call
(
'ZREMRANGEBYSCORE'
,
KEYS
[
1
]
,
0
,
ARGV
[
1
]
-
ARGV
[
2
]
)
local
burst_used
=
redis
.
call
(
'ZCARD'
,
KEYS
[
1
]
)
local
daily_used
=
tonumber
(
redis
.
call
(
'GET'
,
KEYS
[
2
]
)
or
'0'
)
if
burst_used
>=
tonumber
(
ARGV
[
3
]
)
then
-- Tell caller how long to sleep until oldest entry expires
local
oldest
=
redis
.
call
(
'ZRANGE'
,
KEYS
[
1
]
,
0
,
0
,
'WITHSCORES'
)
return
{
0
,
'BURST'
,
(
oldest
[
2
]
+
ARGV
[
2
]
)
-
ARGV
[
1
]
}
end
if
daily_used
>=
tonumber
(
ARGV
[
4
]
)
then
return
{
0
,
'DAILY'
,
-
1
}
end
redis
.
call
(
'ZADD'
,
KEYS
[
1
]
,
ARGV
[
1
]
,
ARGV
[
5
]
)
redis
.
call
(
'PEXPIRE'
,
KEYS
[
1
]
,
ARGV
[
2
]
+
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:...
|
Claude
|
Claude
|
NULL
|
1959
|
|
1958
|
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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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Retry
You said: Ok, so what will happen each time I make a request?
You said: Ok, so what will happen each time I make a request?
Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?
13:44
Retry
Edit
Copy
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Analyzed per-request overhead concerns and quantified Redis performance impact
Analyzed per-request overhead concerns and quantified Redis performance impact
Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out. But the overhead is much smaller than it sounds, and importantly, it's nowhere near your bottleneck.
What "two buckets per request" actually costs
What "two buckets per request" actually costs
Each request makes
one round trip to Redis
— not two — because you fold both bucket checks into a single Lua script that runs atomically server-side:
Copy to clipboard
lua
-- KEYS[1] = burst bucket key (or search)
-- KEYS[2] = daily bucket key
-- ARGV[1] = now (ms) ARGV[2] = window_ms
-- ARGV[3] = bucket_max ARGV[4] = daily_max
-- ARGV[5] = request_id ARGV[6] = daily_ttl
-- Trim sliding window
redis
.
call
(
'ZREMRANGEBYSCORE'
,
KEYS
[
1
]
,
0
,
ARGV
[
1
]
-
ARGV
[
2
]
)
local
burst_used
=
redis
.
call
(
'ZCARD'
,
KEYS
[
1
]
)
local
daily_used
=
tonumber
(
redis
.
call
(
'GET'
,
KEYS
[
2
]
)
or
'0'
)
if
burst_used
>=
tonumber
(
ARGV
[
3
]
)
then
-- Tell caller how long to sleep until oldest entry expires
local
oldest
=
redis
.
call
(
'ZRANGE'
,
KEYS
[
1
]
,
0
,
0
,
'WITHSCORES'
)
return
{
0
,
'BURST'
,
(
oldest
[
2
]
+
ARGV
[
2
]
)
-
ARGV
[
1
]
}
end
if
daily_used
>=
tonumber
(
ARGV
[
4
]
)
then
return
{
0
,
'DAILY'
,
-
1
}
end
redis
.
call
(
'ZADD'
,
KEYS
[
1
]
,
ARGV
[
1
]
,
ARGV
[
5
]
)
redis
.
call
(
'PEXPIRE'
,
KEYS
[
1
]
,
ARGV
[
2
]
+
1000
)
local
d
=
redis
.
call
(
'INCR'
,
KEYS
[
2
]
)
if
d
==
1
then
redis
.
call
(
'EXPIRE'
,
KEYS
[
2
]
,
ARGV
[
6
]
)
end
return...
|
Claude
|
Claude
|
NULL
|
1958
|
|
1957
|
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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
Show more
10:42
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Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
Copy
Give positive feedback
Give negative feedback
Retry
You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
Retry
Edit
Copy
Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
Copy to clipboard
php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.
Copy
Give positive feedback
Give negative feedback
Retry
You said: Ok, so what will happen each time I make a request?
You said: Ok, so what will happen each time I make a request?
Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?
13:44
Retry
Edit
Copy
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Analyzed per-request overhead concerns and quantified Redis performance impact
Analyzed per-request overhead concerns and quantified Redis performance impact
Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out. But the overhead is much smaller than it sounds, and importantly, it's nowhere near your bottleneck.
What "two buckets per request" actually costs
What "two buckets per request" actually costs
Each request makes
one round trip to Redis
— not two — because you fold both bucket checks into a single Lua script that runs atomically server-side:
Copy to clipboard
lua
-- KEYS[1] = burst bucket key (or search)
-- KEYS[2] = daily bucket key
-- ARGV[1] = now (ms) ARGV[2] = window_ms
-- ARGV[3] = bucket_max ARGV[4] = daily_max
-- ARGV[5] = request_id ARGV[6] = daily_ttl
-- Trim sliding window
redis
.
call
(
'ZREMRANGEBYSCORE'
,
KEYS
[
1
]
,
0
,
ARGV
[
1
]
-
ARGV
[
2
]
)
local
burst_used
=
redis
.
call
(
'ZCARD'
,
KEYS
[
1
]
)
local
daily_used
=
tonumber
(
redis
.
call
(
'GET'
,
KEYS
[
2
]
)
or
'0'
)
if
burst_used
>=
tonumber
(
ARGV
[
3
]
)
then
-- Tell caller how long to sleep until oldest entry expires
local
oldest
=
redis
.
call
(
'ZRANGE'
,
KEYS
[
1
]
,
0
,
0
,
'WITHSCORES'
)
return
{
0
,
'BURST'
,
(
oldest
[
2
]
+
ARGV
[
2
]
)
-
ARGV
[
1
]
}
end
if
daily_used
>=
tonumber
(
ARGV
[
4
]
)
then
return
{
0
,
'DAILY'
,
-
1
}
end
redis
.
call
(
'ZADD'
,
KEYS
[
1
]
,
ARGV
[
1
]
,
ARGV
[
5
]
)
redis
.
call
(
'PEXPIRE'
,
KEYS
[
1
]
,
ARGV
[
2
]
+
1000
)
local
d
=
redis
.
call
(
'INCR'
,
KEYS
[
2
]
)
if
d
==
1
then
redis
.
call
(
'EXPIRE'
,
KEYS
[
2
]
,
ARGV
[
6
]
)
end
return
{
1
,
'OK'
,
tonumber
(
ARGV
[
3
]
)
-
burst_used
-
1
}
One
EVALSHA
call. Two keys touched. Returns either
{1, OK, remaining}
or
{0, reason, retry_ms}
. No race conditions because Lua is single-threaded inside Redis. No "check then increment" gap that other workers can sneak through.
The math on whether this is heavy
The math on whether this is heavy
For your 100,000 requests, with 100 PATCHes batched into 1 batch_update call per 100 deals, you're realistically making more like
1,000–2,000 actual API calls
(assuming you're using
/batch/update
and
/batch/read
properly). But let's pretend you really make all 100k.
At HubSpot's ceiling of ~240 calls/sec across both buckets, 100k calls take roughly
7 minutes
of wall time. That means:
Redis ops/sec:
~240. A laptop-grade Redis instance handles 50,000+. You're using under half a percent.
Per-request overhead:
~0.3–1.0 ms for an
EVALSHA
against local-network Redis.
Per HubSpot call:
80–400 ms typical latency.
So the limiter adds something like
0.3% to each request's wall time
. The rate limit itself is forcing you to wait 4–40 ms between calls anyway; adding 0.5 ms of bookkeeping is invisible.
This is a pattern worth internalizing:
the rate limiter cannot be the bottleneck
, because the thing it's gating (HubSpot itself) is two to three orders of magnitude slower than the gating mechanism.
Things that would make it heavy (and how to avoid them)
Things that would make it heavy (and how to avoid them)
Multiple round trips per request
— if you
GET
then
INCR
then
ZADD
separately, you've tripled the latency and broken atomicity. Use Lua. One trip.
No connection pooling
— opening a TCP connection to Redis per request is 10× more expensive than the Redis op itself. Laravel's Redis facade pools by default; just don't fight it.
Persisting accounting to MySQL
— DB writes per API call would absolutely melt under 240/sec. Redis only. If you want analytics, sample or aggregate after the fact.
Synchronous polling waits
— if a worker is denied and busy-loops calling
acquire
every 10ms, you're putting 100/sec of pressure on Redis per worker for nothing. Either sleep for the...
|
Claude
|
Claude
|
NULL
|
1957
|
|
1956
|
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HubSpot rate limit implementation strategy
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Claude is responding
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
Show more
10:42
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
Analyzed per-request overhead concerns and quantified Redis performance impact
Analyzed per-request overhead concerns and quantified Redis performance impact
Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out. But the overhead is much smaller than it sounds, and importantly, it's nowhere near your bottleneck.
What "two buckets per request" actually costs
What "two buckets per request" actually costs
Each request makes
one round trip to Redis
— not two — because you fold both bucket checks into a single Lua script that runs atomically server-side:
Copy to clipboard
lua
-- KEYS[1] = burst bucket key (or search)
-- KEYS[2] = daily bucket key
-- ARGV[1] = now (ms) ARGV[2] = window_ms
-- ARGV[3] = bucket_max ARGV[4] = daily_max
-- ARGV[5] = request_id ARGV[6] = daily_ttl
-- Trim sliding window
redis
.
call
(
'ZREMRANGEBYSCORE'
,
KEYS
[
1
]
,
0
,
ARGV
[
1
]
-
ARGV
[
2
]
)
local
burst_used
=
redis
.
call
(
'ZCARD'
,
KEYS
[
1
]
)
local
daily_used
=
tonumber
(
redis
.
call
(
'GET'
,
KEYS
[
2
]
)
or
'0'
)
if
burst_used
>=
tonumber
(
ARGV
[
3
]
)
then
-- Tell caller how long to sleep until oldest entry expires
local
oldest
=
redis
.
call
(
'ZRANGE'
,
KEYS
[
1
]
,
0
,
0
,
'WITHSCORES'
)
return
{
0
,
'BURST'
,
(
oldest
[
2
]
+
ARGV
[
2
]
)
-
ARGV
[
1
]
}
end
if
daily_used
>=
tonumber
(
ARGV
[
4
]
)
then
return
{
0
,
'DAILY'
,
-
1
}
end
redis
.
call
(
'ZADD'
,
KEYS
[
1
]
,
ARGV
[
1
]
,
ARGV
[
5
]
)
redis
.
call
(
'PEXPIRE'
,
KEYS
[
1
]
,
ARGV
[
2
]
+
1000
)
local
d
=
redis
.
call
(
'INCR'
,
KEYS
[
2
]
)
if
d
==
1
then
redis
.
call
(
'EXPIRE'
,
KEYS
[
2
]
,
ARGV
[
6
]
)
end
return
{
1
,
'OK'
,
tonumber
(
ARGV
[
3
]
)
-
burst_used
-
1
}
One
EVALSHA
call. Two keys touched. Returns either
{1, OK, remaining}
or
{0, reason, retry_ms}
. No race conditions because Lua is single-threaded inside Redis. No "check then increment" gap that other workers can sneak through.
The math on whether this is heavy
The math on whether this is heavy
For your 100,000 requests, with 100 PATCHes batched into 1 batch_update call per 100 deals, you're realistically making more like
1,000–2,000 actual API calls
(assuming you're using
/batch/update
and
/batch/read
properly). But let's pretend you really make all 100k.
At HubSpot's ceiling of ~240 calls/sec across both buckets, 100k calls take roughly
7 minutes
of wall time. That means:
Redis ops/sec:
~240. A laptop-grade Redis instance handles 50,000+.
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HubSpot rate limit implementation strategy
More options for HubSpot rate limit implementation strategy
Claude is responding
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
Show more
10:42
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Copy
Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
Copy
Give positive feedback
Give negative feedback
You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
Edit
Copy
Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
Copy to clipboard
php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.
Copy
Give positive feedback
Give negative feedback
You said: Ok, so what will happen each time I make a request?
You said: Ok, so what will happen each time I make a request?
Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?
13:44
Edit
Copy
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
]
)...
|
Claude
|
Claude
|
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|
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|
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HubSpot rate limit implementation strategy, rename chat
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More options for HubSpot rate limit implementation strategy
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
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
]
,
Keep going in Claude Code
Switch to Claude Code and let Claude work directly in your repo, running and testing as it goes.
Open Claude Code
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Model: Opus 4.7 Adaptive
Opus 4.7
Adaptive
Stop response
Claude is AI and can make mistakes. Please double-check responses.
Claude is AI and can make mistakes. Please double-check responses....
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Wispr FlowFileDictationViewEditHelpWindowHubSpot r Wispr FlowFileDictationViewEditHelpWindowHubSpot rate limit implementation strategy$0suppont Dally• In 1h 1om100% 12Thu 7 May 13:44:47AskJiminnykeportAcuivityservice lestst] A HS_Jocal (jiminny@localhost]A console [PROD] X A console [EU]CascadeHubspot Rate Limitinntil is deadline, not delav. Job continues retrvina until now > retryUnti10.(from RateLimitException):+0 ..Ok, so what will happen each time I makerequests to make that are instant. Do I neeon the type? For example, if there is a seathe search bucket and also the daily buckcalv and ourst ouckers.t need to checkboth the daily and search, or burst, respecintrastructure, adding additional load to eAnalyzed per-request overhead concerns and quantified Redis peYes, your understanding is correct — every request must ciit applies to, atomically, defore the call goes out. But the ovsounds, and importantly, it's nowhere near your bottleneckWhat "two buckets per request" actually costsEach request makes one round trip to Redis — not two -coecksmto a sig e Lua scuot tat runs aromicalv server--- KEYS[1] = burst bucket key(or search)-- KEYS[2] = daily bucket key-- ARGV[1] = now (ms)-= AkGV = nucker max-- ARGVI5 = request 1dARGV[2] = window_msAKGV4= danv maARGVI6= daily th-- Trim sliding windowredis.call('ZREMRANGEBYSCORE' , KEYS[1], O, ARGV[1]Jocal burst used = redis.callZCARD'. KEYSI1IDlocal daily used = tonumber(redis.call('GET' , KEYSif burst_used >**Keep going in Claude CodeSwitch to Claude Code and let Claude work directiv in vour reno..running and testing as it goes.Write a message…Ibl Flow (Basic•: Homeou InsightsD Dictionary* SnippetsTr Style*8. Transforms• ScratchpadInsightsYour Usage Your VoiceLimit NavigatorVOICE PROFILEVoice is your tool for dissecting complex rate limit scenarios, often using Claude to question andmap out how different thresholds interact, ensuring seamless operations without hitting criticalerrors.CATCHPHRASEUnlock this with your next milestone.MOST USED WeleD1701 words remainingUpgrade to Pro° Invite your teamGet a free monthộ Settings® HelpThursday at 1 p.m.YOUR PEAK TIME & PLACEThursday at 1 p.m. is when you dive deep into technicalproblem-solving, using Claude to untangle intricateissues and strategize solutions.MOST CORRECTED WORDYour dictations are private and only stored locally. Never shared or stored by Wispr Flow. Your report is based on local data.bin('8827f672-202d-4162-9d04-73ff5f0566a9') = uuid-C66560290101') = uuid;provider = 'hubspot':Open Claude CodeOpus 4.7 AdaptiveClaude is Al and can make mistakes. Please double-check responses.rns the seconds to wait right now before this specitic attemot should retry. Comes from:Limiter:: requestavallablein → max or all bucket avallableino values1 HubSpot's 429 response Retry-After headergetRetryAtteroAbsolute deadline Datelime)Relative delay int secondsAll failures (rate limit + real errors)Keep retrying with backoff() delays until deadlineYes (increments attempt count)Only RateLimitExceptionRelease immediately with specific delayNo (release() doesn't increment attempts)jether in DeleteCrmEntityTrait:te limit → HandleRateLimit middleware catches itback to queue, attempt count stays the sameTter 10 seconds3 times with RFAI errors (not rate limit).ries, checks retryUntil()1 hour, keeps retryinghour. iob marked failed-1 hour): Ensures we don't give up on deletes due to temporary issues (including sustained rate limiting)mit + aetRetrvAfter@: Ensures we don't burn through Stries durina rate limit storms11, 3 rate limit hits would exhaust stries = 3 and fail the job. With retryUntil, those rate limit releases don't count against the try• can keen trving for an hourTall a4 files with changesapp/Jobs/Middleware/ HandleRateLimit.php +42app/Jobs/Crm/Delete/[ DeleteCrmEntityTrait.php +18View allReiect alllAccent alliOK review documatation again to make sure we coveif 4 spaces...
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Wispr Flow FileDictationView EditHelpWindowHubSpot Wispr Flow FileDictationView EditHelpWindowHubSpot rate limit implementation strategy$0suppont Dally• In 1h 1omst] A HS_local (jiminny@Iocalhost] console [PROD] X 4 console (EU)CascadeHubspot Rate Limiting100% 12Thu 7 May 13:44:35+0 ..ntil is deadline, not delav. Job continues retrvina until now > retryUnti10.rrom RateLimitExcent ionOk, so what will happen each time I makerequests to make that are instant. Do I neeon the type? For example, if there is a seathe search bucket and also the daily buckcallv and ourst ouckers. r need to checkboth the daily and search, or burst, respecintrastructure, adding additional load to e** Quantifying Redis overhead versus HubSpot bottleneck consMOST CORRECTED WORDKeep going in Claude CodeSwitch to Claude Code and let Claude work directiv in vour reno..running and testing as it goes.Write a message…Ibl Flow (Basic•: Homelol Insights@ DictionaryX SnippetsTr Style*8. Transforms• Scratchpad1701 words remainingYou get 2000 words perweek. Upgrade for unlimitedaccessUpgrade to Pro&, Invite your team# Get a free monthSettings® HelpOpen Claude CodeOpus 4.7 AdaptiveInsightsYour Usage Your VoiceCreated May 7,2026Limit NavigatorVOICE PROFILEVoice is your tool for dissecting complex rate limit scenarios, often using Claude to question andmap out how different thresholds interact, ensuring seamless operations without hitting criticalerrors.CATCHPHRASEMOST USED WORDNext update in 10,000 more words©Thursday at 1 p.m.YOUR PEAK TIME & PLACEThursday at 1 p.m. is when you dive deep into technicalproblem-solving, using Claude to untangle intricateissues and strategize solutions.bin('8827f672-202d-4162-9d04-73ff5f0566a9') = uuid-C66560290101') = uuid;provider = 'hubspot':Claude is Al and can make mistakes. Please double-check responses.rns the seconds to wait right now before this specitic attemot should retry. Comes from:Limiter:: requestavallablein → max or all bucket avallableino valuesn HubSpot's 429 response Retry-After headergetRetryAtterAbsolute deadline (DateTime)Relative delay int secondsAll failures (rate limit + real errors)Keep retrying with backoff() delays until deadlineYes (increments attempt count)Only RateLimitExceptionRelease immediately with specific delayNo (release() doesn't increment attempts)gether in DeleteCrmEntityTrait:te limit → HandleRateLimit middleware catches itback to queue, attempt count stays the sameTter 10 seconds3 times with RFAI errors (not rate limit):ries, checks retryUntil()1 hour, keeps retryingihour. iob marked failed-1 hour): Ensures we don't give up on deletes due to temporary issues (including sustained rate limiting)mit + aetRetrvAfter@: Ensures we don't burn through Stries durina rate limit storms1, 3 rate limit hits would exhaust Stries = 3 and fail the job. With retryUntil, those rate limit releases don't count against the try• can keen trving for an hourTall a4 files with changesapp/Jobs/Middleware/ D HandleRateLimit.php +42app/Jobs/Crm/Delete/[ DeleteCrmEntityTrait.php +18View allAccent allOK review documatation again to make sure we coverf 4 spaces...
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ClaudeFileEditVIewWindowHelp= HubSpot rate limit i ClaudeFileEditVIewWindowHelp= HubSpot rate limit implementation strategy vOk, so what will happen each time I make a request? Let's say | have 100,000requests to make that are instant. Do I need to increment each request basedon the tvpe? For example, if there is a search request, I need to increment boththe search bucket and also the daily bucket. If it's a burst, then I can go to thecallv and ourst ouckers. r need to check it it can make a call. do need to seeboth the daily and search, or burst, respectively? Isn't that too heavy for theintrastructure, adding additional load to each request?* Quantifying Redis overhead versus HubSpot bottleneck constraintsKeep going in Claude CodeSwitch to Claude Code and let Claude work directiv in vour reno.running and testing as it goes.Write a message…Opus 4.7 AdaptiveClaudo ic Aland can mako mictakas Plasco double-check recnoncoc& ho local uiminny @localnostA console [PROD] XA console (EU]do jiminny~037 A1 A35 X64 Aovider_id, '@', -1)) AS calendar_domainID c.status = 'active' AND c.calendar_provider_id LIKE 'X@%'EX(c.calendar_provider_id, '@', -1))1<->1.n: on c.user_id = u.id149485; # team 563 crm 53772382; # team 563 crm 53700389; # team 563 crm 53781273; # team 563 crm 53720297: # team 563 crm 537ty_id = 58081273;iquration id = 537 and provider = 'aircali'ted at desciuration 1d = 537 and 1d = 359577591uration_id = 537 and id = 19003658;where id = 1976;d = 5835d = 87714:id = 87714;2.0d04-77££56054La00 = muid| |11You're aLimitNavigatorYour insights are ready!Viejv insights$0Support Daily - in 1h 16m100% 12Inu / May 13.44'34AsKJiminnykepontAcuvityservice lest vCascadeHubspot Rate Limitind+0 ..Important: retrvUntil is deadline. not delav. Job continues retrvina until now > retryUnti10.getRetryAfter() (from RateLimitException):O php// In HandleRateLimit middleware$job->release($e->getRetryAfter());What it does: Returns the seconds to wait right now before this specitic attemot should retry. Comes from:•ProviderRateLimiter:: requestava1lablein → max or all bucket avallableino values• Or parsed from HubSpot's 429 response Retry-After headerThe ditterence:AspectTypeUsed byApplies toBehaviorCounts as try?getRetryAtterAbsolute deadline (DateTime)Relative delay int secondsAll failures (rate limit + real errors)Keep retrying with backoff() delays until deadlineYes (increments attempt count)Only RateLimitExceptionRelease immediately with specific delayNo (release() doesn't increment attempts)How they work together in DeleteCrmEntityTrait:D php// Job hits rate limit → HandleRateLimit middleware catches itJobgoesback to queue, attempt count stays the same/ → Retries arter 10 seconds// If it fails 3 times with REAL errors (not rate limit):tries, checks retryUntil()/ It within 1 hour, keeps retryingiJ- If nast 1 hour. iob marked failedWhy both are needed:• retryUntil(+1 hour): Ensures we don't give up on deletes due to temporary issues (including sustained rate limiting)• HandleRateLimit + aetRetrvAfterO: Ensures we don't burn through Stries durina rate limit stormsWithout retryUnti1, 3 rate limit hits would exhaust stries = 3 and fail the job. With retryUntil, those rate limit releases don't count against the tryoudaet, and the ioo can keen trving for an hourde C!Tall aView allapp/Jobs/Middleware/ HandleRateLimit.php +42ann/lohelCrm/Doloto/ M DolotoCrmEntitvTrit nhn t18Reiect alllAccent allOK review documatation again to make sure we coverf 4 spaces...
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1950
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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 —...
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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
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Quantifying Redis overhead versus HubSpot bottleneck constraints
Quantifying Redis overhead versus HubSpot bottleneck constraints
Quantifying Redis overhead versus HubSpot bottleneck constraints
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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
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Claude
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Claude
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Claude is responding
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
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
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Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
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┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘...
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Claude
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Claude
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NULL
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1947
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SELECT * FROM teams WHERE name LIKE '%litify%'; # 1069, 994, 24993
SELECT * FROM users WHERE id = 25061;
SELECT * FROM crm_profiles WHERE crm_configuration_id = 994;
SELECT * FROM crm_profiles WHERE user_id = 25061;
select * from crm_configurations where id = 834;
SELECT * FROM teams WHERE id = 882;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 882 and sa.provider = 'hubspot';
SELECT * FROM crm_configurations where id = 882; # 933 - GoGlobal
SELECT * FROM opportunities WHERE team_id = 933 order by updated_at desc;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 933 and sa.provider = 'hubspot';
SELECT * FROM crm_configurations WHERE provider = 'hubspot' and crm_provider_id = 7270388;
SELECT * FROM contacts where crm_configuration_id = 834;
SELECT * FROM opportunities WHERE team_id = 933
# AND crm_provider_id IN ('20131586060','46017317898','52543911090','53451356564','54101251892','54323768459');
AND id IN (8482561,18352941,19042734,19232139,19445140,19472541);
SELECT * FROM opportunity_contacts
WHERE opportunity_id IN (8482561,18352941,19042734,19232139,19445140,19472541);
# [PASSWORD_DOTS]
SELECT * FROM crm_configurations where id = 485; #
SELECT * FROM opportunities WHERE team_id = 933 order by updated_at desc;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 933 and sa.provider = 'hubspot';
select crm.provider, l.* from leads l join crm_configurations crm on l.crm_configuration_id = crm.id
where crm.provider NOT IN ('salesforce', 'integration-app', 'bullhorn', 'copper')
# and l.converted_at IS NOT NULL
;
# [PASSWORD_DOTS]
SELECT * FROM activities a WHERE type IN ('email-inbound', 'email-outbound')
and opportunity_id IS NULL
order by id desc;
SELECT * FROM teams WHERE id = 604; # 598
SELECT * FROM activities WHERE id = 74410828; # [EMAIL]
SELECT * FROM accounts WHERE id = 20068382;
SELECT * FROM accounts WHERE id = 35186038;
SELECT * FROM contacts WHERE team_id = 852 and updated_at > '2026-01-23 12:30:00' order by updated_at desc;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 559 and sa.provider = 'hubspot';
SELECT * FROM activities WHERE uuid_to_bin('cb6342b6-a183-401c-b0af-ede92b2ae763') = uuid;
select * from sidekick_settings where team_id = 781;
SELECT * FROM crm_configurations where provider = 'hubspot' and crm_provider_id = 26651871; # Teya
SELECT * FROM crm_configurations where provider = 'hubspot' and crm_provider_id = 7562435;
SELECT * FROM crm_configurations where provider = 'hubspot' and crm_provider_id = 8420347; # opflit 2100
SELECT * FROM crm_layouts WHERE crm_configuration_id = 711;
SELECT * FROM activities where crm_configuration_id = 711 and crm_provider_id IS NULL
and is_internal = 0 and status = 'completed'
order by id desc;
SELECT * FROM crm_layout_entities
WHERE crm_layout_id IN (2352, 2353);
;
SELECT * FROM crm_configurations where provider = 'hubspot' and id = 530;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 556 and sa.provider = 'hubspot';
SELECT * FROM activities WHERE uuid_to_bin('c6ca4b22-7738-4563-a95d-b8a9598924ae') = uuid;
SELECT * FROM activities WHERE uuid_to_bin('442abb2b-28bd-4be8-9c25-19e9bf02766d') = uuid;
select * from contacts
where crm_configuration_id = 530
and crm_provider_id = 872252;
select * from activities where crm_configuration_id = 530
and user_id = 14343 and type like '%softphone%'
and created_at between '2026-01-28 15:00:00' and '2026-01-28 15:10:00';
SELECT * FROM crm_configurations where provider = 'hubspot' and crm_provider_id = 25666868; # Teya
SELECT * FROM crm_configurations where provider = 'hubspot' and crm_provider_id = 8646335; # Teya
SELECT * FROM crm_configurations where provider = 'hubspot' and crm_provider_id IN (5933397);
SELECT t.name, t.id, t.owner_id, c.id, c.provider, c.crm_base_url FROM teams t
JOIN crm_configurations c ON t.id = c.team_id
WHERE t.status = 'active';
SELECT * FROM teams where id = 1091;
SELECT * FROM crm_configurations where team_id = 1091;
SELECT * FROM activity_providers where team_id = 1091;
SELECT * FROM activities where crm_configuration_id = 1024 and type IN ('softphone', 'softphone-outbound')
and provider NOT IN ('hubspot', 'aircall')
# and telephony_provider_id = '019c1131-a22f-4792-b9ea-20adf6a02ed0'
order by id desc;
SELECT * FROM teams WHERE name LIKE '%Leadventure%';
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 1091 and sa.provider = 'salesforce';
SELECT * FROM teams WHERE name LIKE '%Wilson%'; # 862, 812
SELECT * FROM teams where id = 862;
SELECT * FROM crm_configurations where team_id = 862;
SELECT * FROM activity_providers where team_id = 862;
SELECT * FROM activities where crm_configuration_id = 812 and type IN ('softphone', 'softphone-outbound')
and provider NOT IN ('hubspot', 'aircall')
# and telephony_provider_id = '019c1131-a22f-4792-b9ea-20adf6a02ed0'
order by id desc;
SELECT t.id, crm.id, crm.provider, ap.* FROM teams t
join crm_configurations crm on t.id = crm.team_id
join activity_providers ap on t.id = ap.team_id
where t.status = 'active' and ap.is_enabled = 1
and crm.provider = 'hubspot'
and ap.provider NOT IN ('hubspot', 'aircall', 'uploader', 'gong', 'twilio', 'zoom-bot', 'google-meet', 'ms-teams',
'outreach', 'close', 'ringcentral', 'dialpad', 'zoom-phone');
SELECT * FROM teams where id = 1068;
SELECT * FROM crm_configurations where team_id = 1068;
SELECT * FROM activity_providers where team_id = 1068;
SELECT * FROM activities a
where crm_configuration_id = 993 and type IN ('softphone', 'softphone-outbound')
and a.provider NOT IN ('hubspot', 'uploader', 'gong', 'twilio', 'google-meet', 'ms-teams','close'
)
# and telephony_provider_id = '019c1131-a22f-4792-b9ea-20adf6a02ed0'
order by a.id desc;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 1068 and sa.provider = 'hubspot';
# [PASSWORD_DOTS]
# [PASSWORD_DOTS]
# [PASSWORD_DOTS]
SELECT * FROM crm_configurations where id = 882; # 933 - GoGlobal , portalId: 6017093
SELECT * FROM opportunities WHERE team_id = 933 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 933 and updated_at > '2026-02-06 00:00:00' order by updated_at desc;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 933 and sa.provider = 'hubspot';
# [PASSWORD_DOTS]
SELECT * FROM crm_configurations where id = 834; # 882 - AnyVan , portalId: 5468262
SELECT * FROM contacts WHERE crm_configuration_id = 834 and updated_at > '2026-03-30 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE crm_configuration_id = 834 and updated_at > '2026-03-04 08:00:00' order by updated_at desc;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 882 and sa.provider = 'hubspot';
select * from crm_layouts where crm_configuration_id = 834;
select * from crm_layout_entities where crm_layout_id = 2780;
select * from crm_fields where id IN (321153,321192,321193,321194);
SELECT * FROM opportunities WHERE crm_configuration_id = 834 and id = 10993426;
# [PASSWORD_DOTS]
SELECT * FROM crm_configurations where id = 988; # 1057 - Teya (543ce4f4-168c-4571-91ea-5b35c253f06f) , portalId: 26651871
SELECT * FROM opportunities WHERE team_id = 1057 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 1057 and updated_at > '2026-02-04 00:00:00' order by updated_at desc;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 1057 and sa.provider = 'hubspot';
SELECT * FROM crm_configurations where id = 533; # 559 - Connectd , portalId: 6710988
SELECT * FROM opportunities WHERE team_id = 559 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 559 and updated_at > '2026-02-04 00:00:00' order by updated_at desc;
SELECT * FROM crm_configurations where id = 801; # 852 - Rise Vision , portalId: 2700250
SELECT * FROM opportunities WHERE team_id = 852 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 852 and updated_at > '2026-02-04 00:00:00' order by updated_at desc; # 6th last
SELECT * FROM crm_configurations where id = 962; # 1034 - evergrowth.io , portalId: 143180990
SELECT * FROM opportunities WHERE team_id = 1034 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 1034 and updated_at > '2026-02-04 00:00:00' order by updated_at desc;
# [PASSWORD_DOTS]
SELECT * FROM crm_configurations where id = 1037; # 1102 - Jibble , portalId: 6649755
SELECT * FROM opportunities WHERE team_id = 1102 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 1102 and updated_at > '2026-02-04 12:30:00' order by updated_at desc; # 8
SELECT * FROM crm_configurations where id = 1015; # 1049 - Travefy , portalId: 48904401
SELECT * FROM opportunities WHERE team_id = 1049 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 1049 and updated_at > '2026-02-04 12:30:00' order by updated_at desc; # 20
SELECT * FROM crm_configurations where id = 64; # 70 - SalaryFinance , portalId: 3404115
SELECT * FROM opportunities WHERE team_id = 70 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 70 and updated_at > '2026-02-04 12:30:00' order by updated_at desc; # 6th last
SELECT * FROM crm_configurations where id = 802; # 853 - Street Group , portalId: 7658438
SELECT * FROM opportunities WHERE team_id = 853 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 853 and updated_at > '2026-02-04 12:30:00' order by updated_at desc; # 10
SELECT * FROM crm_configurations where id = 872; # 921 - In Professional Development , portalId: 9238273
SELECT * FROM opportunities WHERE team_id = 921 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 921 and updated_at > '2026-02-04 12:30:00' order by updated_at desc; # 2
# [PASSWORD_DOTS]
SELECT * FROM crm_configurations where id = 550; # 576 - SeedLegals , portalId: 3028661
SELECT * FROM opportunities WHERE team_id = 576 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 576 and updated_at > '2026-02-05 14:00:00' order by updated_at desc;
SELECT * FROM crm_configurations where id = 989; # 1058 - rtaoutdoor.com , portalId: 22371204
SELECT * FROM opportunities WHERE team_id = 1058 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 1058 and updated_at > '2026-02-05 14:00:00' order by updated_at desc;
SELECT * FROM crm_configurations where id = 896; # 946 - Mintago , portalId: 6621281
SELECT * FROM opportunities WHERE team_id = 946 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 946 and updated_at > '2026-02-05 14:00:00' order by updated_at desc;
SELECT * FROM crm_configurations where id = 617; # 641 - PCS , portalId: 5244937
SELECT * FROM opportunities WHERE team_id = 641 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 641 and updated_at > '2026-02-05 14:00:00' order by updated_at desc; # 7th
# [PASSWORD_DOTS]
SELECT * FROM crm_configurations where id = 649; # 670 - Eventeny , portalId: 4492849
SELECT * FROM opportunities WHERE team_id = 670 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 670 and updated_at > '2026-02-09 08:00:00' order by updated_at desc; #
SELECT * FROM crm_configurations where id = 48; # 51 - CleanCloud , portalId: 4373137
SELECT * FROM opportunities WHERE team_id = 51 and updated_at > '2026-03-04 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 51 and updated_at > '2026-02-09 08:00:00' order by updated_at desc;
select * from users where team_id = 51; # 7783
SELECT * FROM groups WHERE uuid_to_bin('8a8d2cb6-8b55-4fa3-8b5c-5f0e3d8de59a') = uuid; # 1130
select * from activity_searches where user_id = 7783;
select * from activity_search_filters where activity_search_id IN (32291, 32292);
SELECT asf.activity_search_id, asf.id, asf.value
FROM activity_search_filters asf
WHERE asf.filter = 'group_id'
AND asf.value IN (
SELECT CONCAT(
HEX(SUBSTR(uuid, 5, 4)), '-',
HEX(SUBSTR(uuid, 3, 2)), '-',
HEX(SUBSTR(uuid, 1, 2)), '-',
HEX(SUBSTR(uuid, 9, 2)), '-',
HEX(SUBSTR(uuid, 11))
)
FROM groups
WHERE deleted_at IS NOT NULL
);
SELECT * FROM crm_configurations where id = 272; # 290 - Bonham & Brook , portalId: 5705856
SELECT * FROM opportunities WHERE team_id = 290 and updated_at > '2026-02-05 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 290 and updated_at > '2026-02-09 08:00:00' order by updated_at desc; # 6th
# [PASSWORD_DOTS]
SELECT * FROM crm_configurations where provider = 'hubspot';
SELECT * FROM crm_configurations where id = 1056; # 1119 - Chromatic , portalId: 45602133
SELECT * FROM opportunities WHERE team_id = 1119 and remotely_created_at > '2026-02-01 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 1119 and updated_at > '2026-02-09 09:00:00' order by updated_at desc; # null
# [PASSWORD_DOTS]
select * from contacts where crm_provider_id = '003Uu00000ojD4NIAU';
select
cp.*
# DISTINCT t.id
# cp.id, cp.user_id, t.id, cp.crm_configuration_id, cp.contact_fields
FROM crm_profiles cp
JOIN crm_configurations crm on crm.id = cp.crm_configuration_id
JOIN users u on u.id = cp.user_id
JOIN teams t ON t.id = crm.team_id
WHERE crm.provider = 'salesforce' and t.status = 'active'
and cp.archived_at IS NULL and u.deleted_at IS NULL
and t.id NOT IN (1093)
and t.id = 2
and cp.contact_fields IS NULL;
# and c.crm_provider_id = '003Uu00000ojD4NIAU';
SELECT * FROM users WHERE id = 26484;
SELECT * FROM crm_profiles WHERE user_id = 26484;
SELECT * FROM social_accounts WHERE sociable_id = 26484;
SELECT * FROM crm_configurations where provider = 'salesforce';
select * from users where id IN (10022, 10403);
select * from users where team_id IN (526);
select * from teams where id IN (526, 532);
select * from crm_configurations where id IN (500, 516);
select * from crm_profiles where crm_configuration_id IN (500, 516) and user_id IN (10022, 10403);
select * from contacts where crm_configuration_id IN (500, 516) and crm_provider_id = '003Uu00000ojD4NIAU';
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 526 and sa.provider = 'salesforce';
select * from team_settings where team_id IN (526, 532);
select * from users where id IN (22824);
select * from crm_profiles where crm_configuration_id IN (1026);
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 1093 and sa.provider = 'salesforce';
select * from teams where id = 1099;
select * from users where id = 29643
select * from activity_processing_states;
SELECT * FROM teams where name LIKE '%Fare%'; # 233
SELECT * FROM opportunities where crm_configuration_id = 215
# and crm_provider_id = 'oppo_ogESZf2P50nDrd1nGPvKDXeA6sSaTN5v51Lp4ayVzKR'
;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 1088 and sa.provider = 'hubspot';
SELECT * FROM teams order by updated_at DESC
SELECT * FROM crm_configurations WHERE id = 1019; # SimpleConsign 1088 - no social account
select * from crm_configurations where provider = 'pipedrive';
select * from teams where id = 957;
select * from crm_configurations where id = 957;
SELECT * FROM teams WHERE name LIKE '%Prolific%'; # 544, 518, 10743
SELECT * FROM opportunities where crm_configuration_id = 518 order by id desc;
select * from users where team_id = 1; # 26726 - Gabriela Dureva
SELECT * FROM opportunities where user_id = 26726; # 16834447 - Prolific
select * from activities where user_id = 26726 order by id desc;
select * from contacts where crm_configuration_id = 1
and email IN ('[EMAIL]', '[EMAIL]'); # 2094416, 2093620
SELECT * FROM contacts WHERE id = 6284931;
SELECT p.* FROM activities a JOIN participants p ON a.id = p.activity_id
WHERE a.user_id = 26726 and p.lead_id IN (2094416, 2093620) and a.created_at > '2026-01-01 00:00:00' order by p.email;
select * from activities where id IN (75509259,75509261,75509261,75511034,75026464,75517602,75517605);
select * from crm_configurations where id = 1;
43801692-1aeb-32ce-acba-5b80a479701a
44c3c9cf-6f5e-75f3-8179-bc9f75dd2b1b
405975c0-b3d0-7aaa-821f-09d59cae6dd1
4caf848d-4bed-2299-b248-7788d41f9fca
49bedc3f-f196-eef3-89c3-dea6a3b4aa63
43420989-a09d-b8f8-9806-c8bbf7a02aac
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 1 and sa.provider = 'salesforce';
SELECT * FROM activities WHERE id = 75461988;
SELECT * FROM activities WHERE uuid_to_bin('d6c5052e-e972-49e9-8912-26f2f7d6c5f6') = uuid;
select * from contacts where id = 17900517;
select * from contact_roles cr join crm_configurations crm on cr.crm_configuration_id = crm.id
where crm.provider != 'salesforce';
select * from users where id = 21047;
SELECT * FROM crm_configurations WHERE id = 892;
SELECT * FROM teams WHERE id = 942;
select * from opportunities where team_id = 942 order by updated_at desc;
select * from contacts where team_id = 942 order by updated_at desc;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 942 and sa.provider = 'hubspot';
SELECT * FROM opportunities where team_id = 1 and crm_provider_id IN ('006Pq00000NeH6XIAV', '006Pq000007z8kdIAA'); # 10697889, 6621430
SELECT * FROM crm_configurations WHERE id = 1;
SELECT * FROM teams WHERE crm_id = 1;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 1 and sa.provider = 'salesforce';
select id, user_id, opportunity_fields from crm_profiles where crm_configuration_id = 1
SELECT * FROM opportunities where team_id = 1 order by updated_at desc; # 10697889, 6621430
select * from teams where id = 852;
select * from groups where id = 2286;
select * from sidekick_settings where team_id = 852;
select * from default_activity_types where team_id = 852;
SELECT cc.provider, cc.id, p.id, u.*
FROM users u
LEFT JOIN crm_profiles p ON u.id = p.user_id AND p.id IS NULL -- no profile
INNER JOIN teams t ON u.team_id = t.id AND t.status = 'active' -- team is active
INNER JOIN crm_configurations cc ON t.crm_id = cc.id
WHERE u.status = 1 AND u.deleted_at IS NULL
AND u.crm_required = 1
AND u.team_id = 1
ORDER BY u.team_id;
SELECT * FROM crm_profiles cp where cp.crm_configuration_id = 1 and cp.user_id IN (
18481
);
SELECT cc.provider, cc.id, p.id, u.*
FROM users u
LEFT JOIN crm_profiles p ON u.id = p.user_id
INNER JOIN teams t ON u.team_id = t.id AND t.status = 'active'
INNER JOIN crm_configurations cc ON t.crm_id = cc.id
WHERE u.status = 1
AND u.deleted_at IS NULL
AND u.crm_required = 1
# AND u.team_id = 1
AND p.id IS NULL -- Move this condition to WHERE clause
ORDER BY u.team_id;
SELECT * FROM opportunities WHERE id = 20002609;
select * from teams where id = 1122; # Velatir, 29953 - [EMAIL]
select * from crm_configurations where id = 1060;
select * from crm_layouts where crm_configuration_id = 1060;
SELECT * FROM crm_layout_entities WHERE crm_layout_id = 3596;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 1122 and sa.provider = 'hubspot';
select * from opportunities where team_id = 1122 order by updated_at desc;
select * from crm_field_data where object_type = 'contact';
SELECT * FROM activities WHERE uuid_to_bin('374fc8ed-3315-4c9f-9b25-318b7fd2928f') = uuid; # 76584262
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 248 and sa.provider = 'salesforce';
SELECT * FROM crm_profiles where user_id = 24115; # 005QF000002CswMYAS
SELECT * FROM users where id = 24115;
SELECT * FROM accounts where id = 4002896;
SELECT * FROM teams WHERE name LIKE '%adswerve%';
SELECT * FROM opportunities where crm_configuration_id = 230 AND crm_provider_id IN ("0069N000003GIQ9QAO","0061r000019yGP9AAM","0066900001S2KWlAAN","0066900001TDpj2AAD","0066900001b8uEwAAI","0069N000001rQi0QAE","006QF00000KD40mYAD","006QF00000LzpRJYAZ","0069N000002uomtQAA","0069N000002xlMLQAY","0066900001NV6ubAAD","0061r00001HJp45AAD","006QF00000uTlUoYAK","006QF00000v0bZqYAI");
SELECT * FROM opportunities WHERE crm_configuration_id = 230 AND crm_provider_id = '0069N000003GIQ9QAO'; # 6272203
SELECT u.id, u.email, ac.name, a.* FROM activities a
JOIN users u ON a.user_id = u.id
JOIN accounts ac ON a.account_id = ac.id
WHERE
uuid_to_bin('e3269598-b562-44fb-b5e9-9d2694dc63e0') = a.uuid or
uuid_to_bin('66ddc3ab-4e15-45aa-af0c-248c1eece593') = a.uuid or
uuid_to_bin('826bd328-e1cc-4213-b8d8-572454cacc07') = a.uuid;
select * from users where id = 5825;
SELECT * FROM activities WHERE uuid_to_bin('e56aa2e8-231a-421b-ab1f-cb38ed2bf573') = uuid;
select * from activities where uuid_to_bin('91e13b2f-2d1b-45f8-b1fd-1141b6563782') = uuid;
19594, 862
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 862 and sa.provider = 'salesforce';
select * from automated_reports where id = 36;
select ar.frequency, r.*, ar.* from automated_report_results r
join automated_reports ar on r.report_id = ar.id
where ar.frequency != 'one_off';
select s.* from activity_searches s join users u ON s.user_id = u.id where u.team_id = 882;
select * from nudges n where n.activity_search_id
select * from teams where created_at > '2026-03-09';
SELECT * FROM crm_layouts WHERE crm_configuration_id = 1065; # 1065
SELECT * FROM crm_layout_entities WHERE crm_layout_id = 3617;
select * from users where team_id = 1 and name like '%Lukas%'; # 7160
SELECT * FROM teams WHERE id = 575;
select * from opportunities where team_id = 575;
SELECT * FROM teams WHERE name LIKE '%Integrum ESG%'; # 1126, 1065,
select * from opportunities where team_id = 1126;
SELECT * FROM teams WHERE name LIKE '%Base%'; # 1125, 1063,
select * from opportunities where team_id = 1125;
select * from contacts c
where c.team_id = 882;
SELECT * FROM activities WHERE id = 76822967;
SELECT * FROM crm_profiles WHERE user_id = 15440;
SELECT * FROM crm_profiles WHERE crm_configuration_id = 555;
SELECT * FROM crm_configurations WHERE id = 555;
SELECT * FROM users WHERE id = 15440; # team. 581, gr. 15440, pl. 3911, act. field 162182
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 581 and sa.provider = 'salesforce';
SELECT * FROM automated_report_results order by id desc;
select * from features;
select * from team_features where feature_id = 40;
select * from teams where id = 556;
select * from automated_reports;
where id = 54; # 4fdd41f6-dcf0-30d0-b339-7345381b6044 , ["pdf","podcast"]
SELECT * FROM automated_report_results WHERE uuid_to_bin('822fa41b-afd3-43a9-a248-86b0e36f3131') = uuid;
select * from automated_report_results order by id desc;
SELECT * FROM automated_report_results WHERE id = 1919;
select * from automated_report_results WHERE report_id = 54;
select * from opportunities where id = 7594349;
SELECT * FROM teams WHERE name LIKE '%Les%'; # 711, 692, 16067 - [EMAIL]
select * from playbooks where team_id = 711; # event 226147
SELECT * FROM playbook_categories WHERE playbook_id = 5515;
SELECT * FROM crm_fields WHERE crm_configuration_id = 692 and object_type = 'event';
SELECT * FROM crm_fields WHERE id = 226147;
SELECT * FROM crm_field_values WHERE crm_field_id = 226147;
SELECT * FROM crm_configurations WHERE id = 692;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 711 and sa.provider = 'salesforce';
SELECT * FROM crm_profiles cp JOIN users u on u.id = cp.user_id WHERE u.team_id = 711;
select * from leads;
select * from calendars;
SELECT
t.id AS team_id,
t.name,
LOWER(SUBSTRING_INDEX(c.calendar_provider_id, '@', -1)) AS calendar_domain
FROM teams t
JOIN users u ON u.team_id = t.id
JOIN calendars c ON c.user_id = u.id AND c.status = 'active' AND c.calendar_provider_id LIKE '%@%'
LEFT JOIN team_domains td
ON td.team_id = t.id
AND td.deleted_at IS NULL
AND td.domain = LOWER(SUBSTRING_INDEX(c.calendar_provider_id, '@', -1))
GROUP BY t.id, t.name, calendar_domain
ORDER BY t.name, calendar_domain;
select * from users u join calendars c on c.user_id = u.id
where u.team_id = 882;
select * from activities where id = 74049485; # team 563 crm 537
select * from activities where id = 73272382; # team 563 crm 537
select * from activities where id = 64400389; # team 563 crm 537
select * from activities where id = 58081273; # team 563 crm 537
select * from activities where id = 54520297; # team 563 crm 537
select * from participants where activity_id = 58081273;
select * from activities where crm_configuration_id = 537 and provider = 'aircall'
and account_id = 19003658 order by updated_at desc;
select * from contacts where crm_configuration_id = 537 and id = 35957759;
select * from accounts where crm_configuration_id = 537 and id = 19003658;
select * from automated_report_results where id = 1976;
select * from automated_reports where id = 583;
select * from activity_searches where id = 87714;
select * from activity_search_filters where activity_search_id = 87714;
SELECT * FROM activities WHERE uuid_to_bin('8827f672-202d-4162-9d04-73ff5f0566a9') = uuid
or uuid_to_bin('47842446-af51-4bcb-854f-cc6560290101') = uuid;
SELECT * FROM crm_configurations WHERE provider = 'hubspot';
select * from rate_limits;
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<?php
declare(strict_types=1);
namespace Jiminny\Models;
use Illuminate\Database\Eloquent\Relations\MorphTo;
use Jiminny\Component\Eloquent\Builder;
use Jiminny\Contracts\Http\RateLimitInterface;
/**
* Jiminny\Models\RateLimit
*
* @property int $id
* @property \Illuminate\Support\Carbon|null $created_at
* @property \Illuminate\Support\Carbon|null $updated_at
* @property int $quota
* @property int $window
* @property int $limited_id
* @property string $limited_type
* @property-read \Illuminate\Database\Eloquent\Model $limited
*
* @method static Builder|RateLimit chunkByIdDesc($count, callable $callback, $column = null, $alias = null)
* @method static Builder|RateLimit newModelQuery()
* @method static Builder|RateLimit newQuery()
* @method static Builder|RateLimit query()
* @method static Builder|RateLimit whereCreatedAt($value)
* @method static Builder|RateLimit whereId($value)
* @method static Builder|RateLimit whereLimitedId($value)
* @method static Builder|RateLimit whereLimitedType($value)
* @method static Builder|RateLimit whereQuota($value)
* @method static Builder|RateLimit whereUpdatedAt($value)
* @method static Builder|RateLimit whereWindow($value)
*
* @mixin \Eloquent
*/
class RateLimit extends Model implements RateLimitInterface
{
protected $fillable = [
'quota',
'window',
];
public function limited(): MorphTo
{
return $this->morphTo();
}
public function getQuota(): int
{
return $this->attributes['quota'];
}
public function getWindow(): int
{
return $this->attributes['window'];
}
public function getKey(): string
{
$key = sprintf(
'%s.%s.%s.%s',
$this->limited_id,
$this->limited_type,
$this->getQuota(),
$this->getWindow()
);
return base64_encode($key);
}
}
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SELECT * FROM teams WHERE name LIKE '%litify%'; # 1069, 994, 24993
SELECT * FROM users WHERE id = 25061;
SELECT * FROM crm_profiles WHERE crm_configuration_id = 994;
SELECT * FROM crm_profiles WHERE user_id = 25061;
select * from crm_configurations where id = 834;
SELECT * FROM teams WHERE id = 882;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 882 and sa.provider = 'hubspot';
SELECT * FROM crm_configurations where id = 882; # 933 - GoGlobal
SELECT * FROM opportunities WHERE team_id = 933 order by updated_at desc;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 933 and sa.provider = 'hubspot';
SELECT * FROM crm_configurations WHERE provider = 'hubspot' and crm_provider_id = 7270388;
SELECT * FROM contacts where crm_configuration_id = 834;
SELECT * FROM opportunities WHERE team_id = 933
# AND crm_provider_id IN ('20131586060','46017317898','52543911090','53451356564','54101251892','54323768459');
AND id IN (8482561,18352941,19042734,19232139,19445140,19472541);
SELECT * FROM opportunity_contacts
WHERE opportunity_id IN (8482561,18352941,19042734,19232139,19445140,19472541);
# [PASSWORD_DOTS]
SELECT * FROM crm_configurations where id = 485; #
SELECT * FROM opportunities WHERE team_id = 933 order by updated_at desc;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 933 and sa.provider = 'hubspot';
select crm.provider, l.* from leads l join crm_configurations crm on l.crm_configuration_id = crm.id
where crm.provider NOT IN ('salesforce', 'integration-app', 'bullhorn', 'copper')
# and l.converted_at IS NOT NULL
;
# [PASSWORD_DOTS]
SELECT * FROM activities a WHERE type IN ('email-inbound', 'email-outbound')
and opportunity_id IS NULL
order by id desc;
SELECT * FROM teams WHERE id = 604; # 598
SELECT * FROM activities WHERE id = 74410828; # [EMAIL]
SELECT * FROM accounts WHERE id = 20068382;
SELECT * FROM accounts WHERE id = 35186038;
SELECT * FROM contacts WHERE team_id = 852 and updated_at > '2026-01-23 12:30:00' order by updated_at desc;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 559 and sa.provider = 'hubspot';
SELECT * FROM activities WHERE uuid_to_bin('cb6342b6-a183-401c-b0af-ede92b2ae763') = uuid;
select * from sidekick_settings where team_id = 781;
SELECT * FROM crm_configurations where provider = 'hubspot' and crm_provider_id = 26651871; # Teya
SELECT * FROM crm_configurations where provider = 'hubspot' and crm_provider_id = 7562435;
SELECT * FROM crm_configurations where provider = 'hubspot' and crm_provider_id = 8420347; # opflit 2100
SELECT * FROM crm_layouts WHERE crm_configuration_id = 711;
SELECT * FROM activities where crm_configuration_id = 711 and crm_provider_id IS NULL
and is_internal = 0 and status = 'completed'
order by id desc;
SELECT * FROM crm_layout_entities
WHERE crm_layout_id IN (2352, 2353);
;
SELECT * FROM crm_configurations where provider = 'hubspot' and id = 530;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 556 and sa.provider = 'hubspot';
SELECT * FROM activities WHERE uuid_to_bin('c6ca4b22-7738-4563-a95d-b8a9598924ae') = uuid;
SELECT * FROM activities WHERE uuid_to_bin('442abb2b-28bd-4be8-9c25-19e9bf02766d') = uuid;
select * from contacts
where crm_configuration_id = 530
and crm_provider_id = 872252;
select * from activities where crm_configuration_id = 530
and user_id = 14343 and type like '%softphone%'
and created_at between '2026-01-28 15:00:00' and '2026-01-28 15:10:00';
SELECT * FROM crm_configurations where provider = 'hubspot' and crm_provider_id = 25666868; # Teya
SELECT * FROM crm_configurations where provider = 'hubspot' and crm_provider_id = 8646335; # Teya
SELECT * FROM crm_configurations where provider = 'hubspot' and crm_provider_id IN (5933397);
SELECT t.name, t.id, t.owner_id, c.id, c.provider, c.crm_base_url FROM teams t
JOIN crm_configurations c ON t.id = c.team_id
WHERE t.status = 'active';
SELECT * FROM teams where id = 1091;
SELECT * FROM crm_configurations where team_id = 1091;
SELECT * FROM activity_providers where team_id = 1091;
SELECT * FROM activities where crm_configuration_id = 1024 and type IN ('softphone', 'softphone-outbound')
and provider NOT IN ('hubspot', 'aircall')
# and telephony_provider_id = '019c1131-a22f-4792-b9ea-20adf6a02ed0'
order by id desc;
SELECT * FROM teams WHERE name LIKE '%Leadventure%';
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 1091 and sa.provider = 'salesforce';
SELECT * FROM teams WHERE name LIKE '%Wilson%'; # 862, 812
SELECT * FROM teams where id = 862;
SELECT * FROM crm_configurations where team_id = 862;
SELECT * FROM activity_providers where team_id = 862;
SELECT * FROM activities where crm_configuration_id = 812 and type IN ('softphone', 'softphone-outbound')
and provider NOT IN ('hubspot', 'aircall')
# and telephony_provider_id = '019c1131-a22f-4792-b9ea-20adf6a02ed0'
order by id desc;
SELECT t.id, crm.id, crm.provider, ap.* FROM teams t
join crm_configurations crm on t.id = crm.team_id
join activity_providers ap on t.id = ap.team_id
where t.status = 'active' and ap.is_enabled = 1
and crm.provider = 'hubspot'
and ap.provider NOT IN ('hubspot', 'aircall', 'uploader', 'gong', 'twilio', 'zoom-bot', 'google-meet', 'ms-teams',
'outreach', 'close', 'ringcentral', 'dialpad', 'zoom-phone');
SELECT * FROM teams where id = 1068;
SELECT * FROM crm_configurations where team_id = 1068;
SELECT * FROM activity_providers where team_id = 1068;
SELECT * FROM activities a
where crm_configuration_id = 993 and type IN ('softphone', 'softphone-outbound')
and a.provider NOT IN ('hubspot', 'uploader', 'gong', 'twilio', 'google-meet', 'ms-teams','close'
)
# and telephony_provider_id = '019c1131-a22f-4792-b9ea-20adf6a02ed0'
order by a.id desc;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 1068 and sa.provider = 'hubspot';
# [PASSWORD_DOTS]
# [PASSWORD_DOTS]
# [PASSWORD_DOTS]
SELECT * FROM crm_configurations where id = 882; # 933 - GoGlobal , portalId: 6017093
SELECT * FROM opportunities WHERE team_id = 933 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 933 and updated_at > '2026-02-06 00:00:00' order by updated_at desc;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 933 and sa.provider = 'hubspot';
# [PASSWORD_DOTS]
SELECT * FROM crm_configurations where id = 834; # 882 - AnyVan , portalId: 5468262
SELECT * FROM contacts WHERE crm_configuration_id = 834 and updated_at > '2026-03-30 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE crm_configuration_id = 834 and updated_at > '2026-03-04 08:00:00' order by updated_at desc;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 882 and sa.provider = 'hubspot';
select * from crm_layouts where crm_configuration_id = 834;
select * from crm_layout_entities where crm_layout_id = 2780;
select * from crm_fields where id IN (321153,321192,321193,321194);
SELECT * FROM opportunities WHERE crm_configuration_id = 834 and id = 10993426;
# [PASSWORD_DOTS]
SELECT * FROM crm_configurations where id = 988; # 1057 - Teya (543ce4f4-168c-4571-91ea-5b35c253f06f) , portalId: 26651871
SELECT * FROM opportunities WHERE team_id = 1057 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 1057 and updated_at > '2026-02-04 00:00:00' order by updated_at desc;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 1057 and sa.provider = 'hubspot';
SELECT * FROM crm_configurations where id = 533; # 559 - Connectd , portalId: 6710988
SELECT * FROM opportunities WHERE team_id = 559 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 559 and updated_at > '2026-02-04 00:00:00' order by updated_at desc;
SELECT * FROM crm_configurations where id = 801; # 852 - Rise Vision , portalId: 2700250
SELECT * FROM opportunities WHERE team_id = 852 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 852 and updated_at > '2026-02-04 00:00:00' order by updated_at desc; # 6th last
SELECT * FROM crm_configurations where id = 962; # 1034 - evergrowth.io , portalId: 143180990
SELECT * FROM opportunities WHERE team_id = 1034 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 1034 and updated_at > '2026-02-04 00:00:00' order by updated_at desc;
# [PASSWORD_DOTS]
SELECT * FROM crm_configurations where id = 1037; # 1102 - Jibble , portalId: 6649755
SELECT * FROM opportunities WHERE team_id = 1102 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 1102 and updated_at > '2026-02-04 12:30:00' order by updated_at desc; # 8
SELECT * FROM crm_configurations where id = 1015; # 1049 - Travefy , portalId: 48904401
SELECT * FROM opportunities WHERE team_id = 1049 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 1049 and updated_at > '2026-02-04 12:30:00' order by updated_at desc; # 20
SELECT * FROM crm_configurations where id = 64; # 70 - SalaryFinance , portalId: 3404115
SELECT * FROM opportunities WHERE team_id = 70 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 70 and updated_at > '2026-02-04 12:30:00' order by updated_at desc; # 6th last
SELECT * FROM crm_configurations where id = 802; # 853 - Street Group , portalId: 7658438
SELECT * FROM opportunities WHERE team_id = 853 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 853 and updated_at > '2026-02-04 12:30:00' order by updated_at desc; # 10
SELECT * FROM crm_configurations where id = 872; # 921 - In Professional Development , portalId: 9238273
SELECT * FROM opportunities WHERE team_id = 921 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 921 and updated_at > '2026-02-04 12:30:00' order by updated_at desc; # 2
# [PASSWORD_DOTS]
SELECT * FROM crm_configurations where id = 550; # 576 - SeedLegals , portalId: 3028661
SELECT * FROM opportunities WHERE team_id = 576 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 576 and updated_at > '2026-02-05 14:00:00' order by updated_at desc;
SELECT * FROM crm_configurations where id = 989; # 1058 - rtaoutdoor.com , portalId: 22371204
SELECT * FROM opportunities WHERE team_id = 1058 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 1058 and updated_at > '2026-02-05 14:00:00' order by updated_at desc;
SELECT * FROM crm_configurations where id = 896; # 946 - Mintago , portalId: 6621281
SELECT * FROM opportunities WHERE team_id = 946 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 946 and updated_at > '2026-02-05 14:00:00' order by updated_at desc;
SELECT * FROM crm_configurations where id = 617; # 641 - PCS , portalId: 5244937
SELECT * FROM opportunities WHERE team_id = 641 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 641 and updated_at > '2026-02-05 14:00:00' order by updated_at desc; # 7th
# [PASSWORD_DOTS]
SELECT * FROM crm_configurations where id = 649; # 670 - Eventeny , portalId: 4492849
SELECT * FROM opportunities WHERE team_id = 670 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 670 and updated_at > '2026-02-09 08:00:00' order by updated_at desc; #
SELECT * FROM crm_configurations where id = 48; # 51 - CleanCloud , portalId: 4373137
SELECT * FROM opportunities WHERE team_id = 51 and updated_at > '2026-03-04 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 51 and updated_at > '2026-02-09 08:00:00' order by updated_at desc;
select * from users where team_id = 51; # 7783
SELECT * FROM groups WHERE uuid_to_bin('8a8d2cb6-8b55-4fa3-8b5c-5f0e3d8de59a') = uuid; # 1130
select * from activity_searches where user_id = 7783;
select * from activity_search_filters where activity_search_id IN (32291, 32292);
SELECT asf.activity_search_id, asf.id, asf.value
FROM activity_search_filters asf
WHERE asf.filter = 'group_id'
AND asf.value IN (
SELECT CONCAT(
HEX(SUBSTR(uuid, 5, 4)), '-',
HEX(SUBSTR(uuid, 3, 2)), '-',
HEX(SUBSTR(uuid, 1, 2)), '-',
HEX(SUBSTR(uuid, 9, 2)), '-',
HEX(SUBSTR(uuid, 11))
)
FROM groups
WHERE deleted_at IS NOT NULL
);
SELECT * FROM crm_configurations where id = 272; # 290 - Bonham & Brook , portalId: 5705856
SELECT * FROM opportunities WHERE team_id = 290 and updated_at > '2026-02-05 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 290 and updated_at > '2026-02-09 08:00:00' order by updated_at desc; # 6th
# [PASSWORD_DOTS]
SELECT * FROM crm_configurations where provider = 'hubspot';
SELECT * FROM crm_configurations where id = 1056; # 1119 - Chromatic , portalId: 45602133
SELECT * FROM opportunities WHERE team_id = 1119 and remotely_created_at > '2026-02-01 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 1119 and updated_at > '2026-02-09 09:00:00' order by updated_at desc; # null
# [PASSWORD_DOTS]
select * from contacts where crm_provider_id = '003Uu00000ojD4NIAU';
select
cp.*
# DISTINCT t.id
# cp.id, cp.user_id, t.id, cp.crm_configuration_id, cp.contact_fields
FROM crm_profiles cp
JOIN crm_configurations crm on crm.id = cp.crm_configuration_id
JOIN users u on u.id = cp.user_id
JOIN teams t ON t.id = crm.team_id
WHERE crm.provider = 'salesforce' and t.status = 'active'
and cp.archived_at IS NULL and u.deleted_at IS NULL
and t.id NOT IN (1093)
and t.id = 2
and cp.contact_fields IS NULL;
# and c.crm_provider_id = '003Uu00000ojD4NIAU';
SELECT * FROM users WHERE id = 26484;
SELECT * FROM crm_profiles WHERE user_id = 26484;
SELECT * FROM social_accounts WHERE sociable_id = 26484;
SELECT * FROM crm_configurations where provider = 'salesforce';
select * from users where id IN (10022, 10403);
select * from users where team_id IN (526);
select * from teams where id IN (526, 532);
select * from crm_configurations where id IN (500, 516);
select * from crm_profiles where crm_configuration_id IN (500, 516) and user_id IN (10022, 10403);
select * from contacts where crm_configuration_id IN (500, 516) and crm_provider_id = '003Uu00000ojD4NIAU';
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 526 and sa.provider = 'salesforce';
select * from team_settings where team_id IN (526, 532);
select * from users where id IN (22824);
select * from crm_profiles where crm_configuration_id IN (1026);
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 1093 and sa.provider = 'salesforce';
select * from teams where id = 1099;
select * from users where id = 29643
select * from activity_processing_states;
SELECT * FROM teams where name LIKE '%Fare%'; # 233
SELECT * FROM opportunities where crm_configuration_id = 215
# and crm_provider_id = 'oppo_ogESZf2P50nDrd1nGPvKDXeA6sSaTN5v51Lp4ayVzKR'
;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 1088 and sa.provider = 'hubspot';
SELECT * FROM teams order by updated_at DESC
SELECT * FROM crm_configurations WHERE id = 1019; # SimpleConsign 1088 - no social account
select * from crm_configurations where provider = 'pipedrive';
select * from teams where id = 957;
select * from crm_configurations where id = 957;
SELECT * FROM teams WHERE name LIKE '%Prolific%'; # 544, 518, 10743
SELECT * FROM opportunities where crm_configuration_id = 518 order by id desc;
select * from users where team_id = 1; # 26726 - Gabriela Dureva
SELECT * FROM opportunities where user_id = 26726; # 16834447 - Prolific
select * from activities where user_id = 26726 order by id desc;
select * from contacts where crm_configuration_id = 1
and email IN ('[EMAIL]', '[EMAIL]'); # 2094416, 2093620
SELECT * FROM contacts WHERE id = 6284931;
SELECT p.* FROM activities a JOIN participants p ON a.id = p.activity_id
WHERE a.user_id = 26726 and p.lead_id IN (2094416, 2093620) and a.created_at > '2026-01-01 00:00:00' order by p.email;
select * from activities where id IN (75509259,75509261,75509261,75511034,75026464,75517602,75517605);
select * from crm_configurations where id = 1;
43801692-1aeb-32ce-acba-5b80a479701a
44c3c9cf-6f5e-75f3-8179-bc9f75dd2b1b
405975c0-b3d0-7aaa-821f-09d59cae6dd1
4caf848d-4bed-2299-b248-7788d41f9fca
49bedc3f-f196-eef3-89c3-dea6a3b4aa63
43420989-a09d-b8f8-9806-c8bbf7a02aac
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 1 and sa.provider = 'salesforce';
SELECT * FROM activities WHERE id = 75461988;
SELECT * FROM activities WHERE uuid_to_bin('d6c5052e-e972-49e9-8912-26f2f7d6c5f6') = uuid;
select * from contacts where id = 17900517;
select * from contact_roles cr join crm_configurations crm on cr.crm_configuration_id = crm.id
where crm.provider != 'salesforce';
select * from users where id = 21047;
SELECT * FROM crm_configurations WHERE id = 892;
SELECT * FROM teams WHERE id = 942;
select * from opportunities where team_id = 942 order by updated_at desc;
select * from contacts where team_id = 942 order by updated_at desc;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 942 and sa.provider = 'hubspot';
SELECT * FROM opportunities where team_id = 1 and crm_provider_id IN ('006Pq00000NeH6XIAV', '006Pq000007z8kdIAA'); # 10697889, 6621430
SELECT * FROM crm_configurations WHERE id = 1;
SELECT * FROM teams WHERE crm_id = 1;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 1 and sa.provider = 'salesforce';
select id, user_id, opportunity_fields from crm_profiles where crm_configuration_id = 1
SELECT * FROM opportunities where team_id = 1 order by updated_at desc; # 10697889, 6621430
select * from teams where id = 852;
select * from groups where id = 2286;
select * from sidekick_settings where team_id = 852;
select * from default_activity_types where team_id = 852;
SELECT cc.provider, cc.id, p.id, u.*
FROM users u
LEFT JOIN crm_profiles p ON u.id = p.user_id AND p.id IS NULL -- no profile
INNER JOIN teams t ON u.team_id = t.id AND t.status = 'active' -- team is active
INNER JOIN crm_configurations cc ON t.crm_id = cc.id
WHERE u.status = 1 AND u.deleted_at IS NULL
AND u.crm_required = 1
AND u.team_id = 1
ORDER BY u.team_id;
SELECT * FROM crm_profiles cp where cp.crm_configuration_id = 1 and cp.user_id IN (
18481
);
SELECT cc.provider, cc.id, p.id, u.*
FROM users u
LEFT JOIN crm_profiles p ON u.id = p.user_id
INNER JOIN teams t ON u.team_id = t.id AND t.status = 'active'
INNER JOIN crm_configurations cc ON t.crm_id = cc.id
WHERE u.status = 1
AND u.deleted_at IS NULL
AND u.crm_required = 1
# AND u.team_id = 1
AND p.id IS NULL -- Move this condition to WHERE clause
ORDER BY u.team_id;
SELECT * FROM opportunities WHERE id = 20002609;
select * from teams where id = 1122; # Velatir, 29953 - [EMAIL]
select * from crm_configurations where id = 1060;
select * from crm_layouts where crm_configuration_id = 1060;
SELECT * FROM crm_layout_entities WHERE crm_layout_id = 3596;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 1122 and sa.provider = 'hubspot';
select * from opportunities where team_id = 1122 order by updated_at desc;
select * from crm_field_data where object_type = 'contact';
SELECT * FROM activities WHERE uuid_to_bin('374fc8ed-3315-4c9f-9b25-318b7fd2928f') = uuid; # 76584262
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 248 and sa.provider = 'salesforce';
SELECT * FROM crm_profiles where user_id = 24115; # 005QF000002CswMYAS
SELECT * FROM users where id = 24115;
SELECT * FROM accounts where id = 4002896;
SELECT * FROM teams WHERE name LIKE '%adswerve%';
SELECT * FROM opportunities where crm_configuration_id = 230 AND crm_provider_id IN ("0069N000003GIQ9QAO","0061r000019yGP9AAM","0066900001S2KWlAAN","0066900001TDpj2AAD","0066900001b8uEwAAI","0069N000001rQi0QAE","006QF00000KD40mYAD","006QF00000LzpRJYAZ","0069N000002uomtQAA","0069N000002xlMLQAY","0066900001NV6ubAAD","0061r00001HJp45AAD","006QF00000uTlUoYAK","006QF00000v0bZqYAI");
SELECT * FROM opportunities WHERE crm_configuration_id = 230 AND crm_provider_id = '0069N000003GIQ9QAO'; # 6272203
SELECT u.id, u.email, ac.name, a.* FROM activities a
JOIN users u ON a.user_id = u.id
JOIN accounts ac ON a.account_id = ac.id
WHERE
uuid_to_bin('e3269598-b562-44fb-b5e9-9d2694dc63e0') = a.uuid or
uuid_to_bin('66ddc3ab-4e15-45aa-af0c-248c1eece593') = a.uuid or
uuid_to_bin('826bd328-e1cc-4213-b8d8-572454cacc07') = a.uuid;
select * from users where id = 5825;
SELECT * FROM activities WHERE uuid_to_bin('e56aa2e8-231a-421b-ab1f-cb38ed2bf573') = uuid;
select * from activities where uuid_to_bin('91e13b2f-2d1b-45f8-b1fd-1141b6563782') = uuid;
19594, 862
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 862 and sa.provider = 'salesforce';
select * from automated_reports where id = 36;
select ar.frequency, r.*, ar.* from automated_report_results r
join automated_reports ar on r.report_id = ar.id
where ar.frequency != 'one_off';
select s.* from activity_searches s join users u ON s.user_id = u.id where u.team_id = 882;
select * from nudges n where n.activity_search_id
select * from teams where created_at > '2026-03-09';
SELECT * FROM crm_layouts WHERE crm_configuration_id = 1065; # 1065
SELECT * FROM crm_layout_entities WHERE crm_layout_id = 3617;
select * from users where team_id = 1 and name like '%Lukas%'; # 7160
SELECT * FROM teams WHERE id = 575;
select * from opportunities where team_id = 575;
SELECT * FROM teams WHERE name LIKE '%Integrum ESG%'; # 1126, 1065,
select * from opportunities where team_id = 1126;
SELECT * FROM teams WHERE name LIKE '%Base%'; # 1125, 1063,
select * from opportunities where team_id = 1125;
select * from contacts c
where c.team_id = 882;
SELECT * FROM activities WHERE id = 76822967;
SELECT * FROM crm_profiles WHERE user_id = 15440;
SELECT * FROM crm_profiles WHERE crm_configuration_id = 555;
SELECT * FROM crm_configurations WHERE id = 555;
SELECT * FROM users WHERE id = 15440; # team. 581, gr. 15440, pl. 3911, act. field 162182
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 581 and sa.provider = 'salesforce';
SELECT * FROM automated_report_results order by id desc;
select * from features;
select * from team_features where feature_id = 40;
select * from teams where id = 556;
select * from automated_reports;
where id = 54; # 4fdd41f6-dcf0-30d0-b339-7345381b6044 , ["pdf","podcast"]
SELECT * FROM automated_report_results WHERE uuid_to_bin('822fa41b-afd3-43a9-a248-86b0e36f3131') = uuid;
select * from automated_report_results order by id desc;
SELECT * FROM automated_report_results WHERE id = 1919;
select * from automated_report_results WHERE report_id = 54;
select * from opportunities where id = 7594349;
SELECT * FROM teams WHERE name LIKE '%Les%'; # 711, 692, 16067 - [EMAIL]
select * from playbooks where team_id = 711; # event 226147
SELECT * FROM playbook_categories WHERE playbook_id = 5515;
SELECT * FROM crm_fields WHERE crm_configuration_id = 692 and object_type = 'event';
SELECT * FROM crm_fields WHERE id = 226147;
SELECT * FROM crm_field_values WHERE crm_field_id = 226147;
SELECT * FROM crm_configurations WHERE id = 692;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 711 and sa.provider = 'salesforce';
SELECT * FROM crm_profiles cp JOIN users u on u.id = cp.user_id WHERE u.team_id = 711;
select * from leads;
select * from calendars;
SELECT
t.id AS team_id,
t.name,
LOWER(SUBSTRING_INDEX(c.calendar_provider_id, '@', -1)) AS calendar_domain
FROM teams t
JOIN users u ON u.team_id = t.id
JOIN calendars c ON c.user_id = u.id AND c.status = 'active' AND c.calendar_provider_id LIKE '%@%'
LEFT JOIN team_domains td
ON td.team_id = t.id
AND td.deleted_at IS NULL
AND td.domain = LOWER(SUBSTRING_INDEX(c.calendar_provider_id, '@', -1))
GROUP BY t.id, t.name, calendar_domain
ORDER BY t.name, calendar_domain;
select * from users u join calendars c on c.user_id = u.id
where u.team_id = 882;
select * from activities where id = 74049485; # team 563 crm 537
select * from activities where id = 73272382; # team 563 crm 537
select * from activities where id = 64400389; # team 563 crm 537
select * from activities where id = 58081273; # team 563 crm 537
select * from activities where id = 54520297; # team 563 crm 537
select * from participants where activity_id = 58081273;
select * from activities where crm_configuration_id = 537 and provider = 'aircall'
and account_id = 19003658 order by updated_at desc;
select * from contacts where crm_configuration_id = 537 and id = 35957759;
select * from accounts where crm_configuration_id = 537 and id = 19003658;
select * from automated_report_results where id = 1976;
select * from automated_reports where id = 583;
select * from activity_searches where id = 87714;
select * from activity_search_filters where activity_search_id = 87714;
SELECT * FROM activities WHERE uuid_to_bin('8827f672-202d-4162-9d04-73ff5f0566a9') = uuid
or uuid_to_bin('47842446-af51-4bcb-854f-cc6560290101') = uuid;
SELECT * FROM crm_configurations WHERE provider = 'hubspot';
select * from rate_limits;
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<?php
declare(strict_types=1);
namespace Jiminny\Models;
use Illuminate\Database\Eloquent\Relations\MorphTo;
use Jiminny\Component\Eloquent\Builder;
use Jiminny\Contracts\Http\RateLimitInterface;
/**
* Jiminny\Models\RateLimit
*
* @property int $id
* @property \Illuminate\Support\Carbon|null $created_at
* @property \Illuminate\Support\Carbon|null $updated_at
* @property int $quota
* @property int $window
* @property int $limited_id
* @property string $limited_type
* @property-read \Illuminate\Database\Eloquent\Model $limited
*
* @method static Builder|RateLimit chunkByIdDesc($count, callable $callback, $column = null, $alias = null)
* @method static Builder|RateLimit newModelQuery()
* @method static Builder|RateLimit newQuery()
* @method static Builder|RateLimit query()
* @method static Builder|RateLimit whereCreatedAt($value)
* @method static Builder|RateLimit whereId($value)
* @method static Builder|RateLimit whereLimitedId($value)
* @method static Builder|RateLimit whereLimitedType($value)
* @method static Builder|RateLimit whereQuota($value)
* @method static Builder|RateLimit whereUpdatedAt($value)
* @method static Builder|RateLimit whereWindow($value)
*
* @mixin \Eloquent
*/
class RateLimit extends Model implements RateLimitInterface
{
protected $fillable = [
'quota',
'window',
];
public function limited(): MorphTo
{
return $this->morphTo();
}
public function getQuota(): int
{
return $this->attributes['quota'];
}
public function getWindow(): int
{
return $this->attributes['window'];
}
public function getKey(): string
{
$key = sprintf(
'%s.%s.%s.%s',
$this->limited_id,
$this->limited_type,
$this->getQuota(),
$this->getWindow()
);
return base64_encode($key);
}
}
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SELECT * FROM teams WHERE name LIKE '%litify%'; # 1069, 994, 24993
SELECT * FROM users WHERE id = 25061;
SELECT * FROM crm_profiles WHERE crm_configuration_id = 994;
SELECT * FROM crm_profiles WHERE user_id = 25061;
select * from crm_configurations where id = 834;
SELECT * FROM teams WHERE id = 882;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 882 and sa.provider = 'hubspot';
SELECT * FROM crm_configurations where id = 882; # 933 - GoGlobal
SELECT * FROM opportunities WHERE team_id = 933 order by updated_at desc;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 933 and sa.provider = 'hubspot';
SELECT * FROM crm_configurations WHERE provider = 'hubspot' and crm_provider_id = 7270388;
SELECT * FROM contacts where crm_configuration_id = 834;
SELECT * FROM opportunities WHERE team_id = 933
# AND crm_provider_id IN ('20131586060','46017317898','52543911090','53451356564','54101251892','54323768459');
AND id IN (8482561,18352941,19042734,19232139,19445140,19472541);
SELECT * FROM opportunity_contacts
WHERE opportunity_id IN (8482561,18352941,19042734,19232139,19445140,19472541);
# [PASSWORD_DOTS]
SELECT * FROM crm_configurations where id = 485; #
SELECT * FROM opportunities WHERE team_id = 933 order by updated_at desc;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 933 and sa.provider = 'hubspot';
select crm.provider, l.* from leads l join crm_configurations crm on l.crm_configuration_id = crm.id
where crm.provider NOT IN ('salesforce', 'integration-app', 'bullhorn', 'copper')
# and l.converted_at IS NOT NULL
;
# [PASSWORD_DOTS]
SELECT * FROM activities a WHERE type IN ('email-inbound', 'email-outbound')
and opportunity_id IS NULL
order by id desc;
SELECT * FROM teams WHERE id = 604; # 598
SELECT * FROM activities WHERE id = 74410828; # [EMAIL]
SELECT * FROM accounts WHERE id = 20068382;
SELECT * FROM accounts WHERE id = 35186038;
SELECT * FROM contacts WHERE team_id = 852 and updated_at > '2026-01-23 12:30:00' order by updated_at desc;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 559 and sa.provider = 'hubspot';
SELECT * FROM activities WHERE uuid_to_bin('cb6342b6-a183-401c-b0af-ede92b2ae763') = uuid;
select * from sidekick_settings where team_id = 781;
SELECT * FROM crm_configurations where provider = 'hubspot' and crm_provider_id = 26651871; # Teya
SELECT * FROM crm_configurations where provider = 'hubspot' and crm_provider_id = 7562435;
SELECT * FROM crm_configurations where provider = 'hubspot' and crm_provider_id = 8420347; # opflit 2100
SELECT * FROM crm_layouts WHERE crm_configuration_id = 711;
SELECT * FROM activities where crm_configuration_id = 711 and crm_provider_id IS NULL
and is_internal = 0 and status = 'completed'
order by id desc;
SELECT * FROM crm_layout_entities
WHERE crm_layout_id IN (2352, 2353);
;
SELECT * FROM crm_configurations where provider = 'hubspot' and id = 530;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 556 and sa.provider = 'hubspot';
SELECT * FROM activities WHERE uuid_to_bin('c6ca4b22-7738-4563-a95d-b8a9598924ae') = uuid;
SELECT * FROM activities WHERE uuid_to_bin('442abb2b-28bd-4be8-9c25-19e9bf02766d') = uuid;
select * from contacts
where crm_configuration_id = 530
and crm_provider_id = 872252;
select * from activities where crm_configuration_id = 530
and user_id = 14343 and type like '%softphone%'
and created_at between '2026-01-28 15:00:00' and '2026-01-28 15:10:00';
SELECT * FROM crm_configurations where provider = 'hubspot' and crm_provider_id = 25666868; # Teya
SELECT * FROM crm_configurations where provider = 'hubspot' and crm_provider_id = 8646335; # Teya
SELECT * FROM crm_configurations where provider = 'hubspot' and crm_provider_id IN (5933397);
SELECT t.name, t.id, t.owner_id, c.id, c.provider, c.crm_base_url FROM teams t
JOIN crm_configurations c ON t.id = c.team_id
WHERE t.status = 'active';
SELECT * FROM teams where id = 1091;
SELECT * FROM crm_configurations where team_id = 1091;
SELECT * FROM activity_providers where team_id = 1091;
SELECT * FROM activities where crm_configuration_id = 1024 and type IN ('softphone', 'softphone-outbound')
and provider NOT IN ('hubspot', 'aircall')
# and telephony_provider_id = '019c1131-a22f-4792-b9ea-20adf6a02ed0'
order by id desc;
SELECT * FROM teams WHERE name LIKE '%Leadventure%';
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 1091 and sa.provider = 'salesforce';
SELECT * FROM teams WHERE name LIKE '%Wilson%'; # 862, 812
SELECT * FROM teams where id = 862;
SELECT * FROM crm_configurations where team_id = 862;
SELECT * FROM activity_providers where team_id = 862;
SELECT * FROM activities where crm_configuration_id = 812 and type IN ('softphone', 'softphone-outbound')
and provider NOT IN ('hubspot', 'aircall')
# and telephony_provider_id = '019c1131-a22f-4792-b9ea-20adf6a02ed0'
order by id desc;
SELECT t.id, crm.id, crm.provider, ap.* FROM teams t
join crm_configurations crm on t.id = crm.team_id
join activity_providers ap on t.id = ap.team_id
where t.status = 'active' and ap.is_enabled = 1
and crm.provider = 'hubspot'
and ap.provider NOT IN ('hubspot', 'aircall', 'uploader', 'gong', 'twilio', 'zoom-bot', 'google-meet', 'ms-teams',
'outreach', 'close', 'ringcentral', 'dialpad', 'zoom-phone');
SELECT * FROM teams where id = 1068;
SELECT * FROM crm_configurations where team_id = 1068;
SELECT * FROM activity_providers where team_id = 1068;
SELECT * FROM activities a
where crm_configuration_id = 993 and type IN ('softphone', 'softphone-outbound')
and a.provider NOT IN ('hubspot', 'uploader', 'gong', 'twilio', 'google-meet', 'ms-teams','close'
)
# and telephony_provider_id = '019c1131-a22f-4792-b9ea-20adf6a02ed0'
order by a.id desc;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 1068 and sa.provider = 'hubspot';
# [PASSWORD_DOTS]
# [PASSWORD_DOTS]
# [PASSWORD_DOTS]
SELECT * FROM crm_configurations where id = 882; # 933 - GoGlobal , portalId: 6017093
SELECT * FROM opportunities WHERE team_id = 933 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 933 and updated_at > '2026-02-06 00:00:00' order by updated_at desc;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 933 and sa.provider = 'hubspot';
# [PASSWORD_DOTS]
SELECT * FROM crm_configurations where id = 834; # 882 - AnyVan , portalId: 5468262
SELECT * FROM contacts WHERE crm_configuration_id = 834 and updated_at > '2026-03-30 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE crm_configuration_id = 834 and updated_at > '2026-03-04 08:00:00' order by updated_at desc;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 882 and sa.provider = 'hubspot';
select * from crm_layouts where crm_configuration_id = 834;
select * from crm_layout_entities where crm_layout_id = 2780;
select * from crm_fields where id IN (321153,321192,321193,321194);
SELECT * FROM opportunities WHERE crm_configuration_id = 834 and id = 10993426;
# [PASSWORD_DOTS]
SELECT * FROM crm_configurations where id = 988; # 1057 - Teya (543ce4f4-168c-4571-91ea-5b35c253f06f) , portalId: 26651871
SELECT * FROM opportunities WHERE team_id = 1057 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 1057 and updated_at > '2026-02-04 00:00:00' order by updated_at desc;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 1057 and sa.provider = 'hubspot';
SELECT * FROM crm_configurations where id = 533; # 559 - Connectd , portalId: 6710988
SELECT * FROM opportunities WHERE team_id = 559 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 559 and updated_at > '2026-02-04 00:00:00' order by updated_at desc;
SELECT * FROM crm_configurations where id = 801; # 852 - Rise Vision , portalId: 2700250
SELECT * FROM opportunities WHERE team_id = 852 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 852 and updated_at > '2026-02-04 00:00:00' order by updated_at desc; # 6th last
SELECT * FROM crm_configurations where id = 962; # 1034 - evergrowth.io , portalId: 143180990
SELECT * FROM opportunities WHERE team_id = 1034 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 1034 and updated_at > '2026-02-04 00:00:00' order by updated_at desc;
# [PASSWORD_DOTS]
SELECT * FROM crm_configurations where id = 1037; # 1102 - Jibble , portalId: 6649755
SELECT * FROM opportunities WHERE team_id = 1102 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 1102 and updated_at > '2026-02-04 12:30:00' order by updated_at desc; # 8
SELECT * FROM crm_configurations where id = 1015; # 1049 - Travefy , portalId: 48904401
SELECT * FROM opportunities WHERE team_id = 1049 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 1049 and updated_at > '2026-02-04 12:30:00' order by updated_at desc; # 20
SELECT * FROM crm_configurations where id = 64; # 70 - SalaryFinance , portalId: 3404115
SELECT * FROM opportunities WHERE team_id = 70 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 70 and updated_at > '2026-02-04 12:30:00' order by updated_at desc; # 6th last
SELECT * FROM crm_configurations where id = 802; # 853 - Street Group , portalId: 7658438
SELECT * FROM opportunities WHERE team_id = 853 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 853 and updated_at > '2026-02-04 12:30:00' order by updated_at desc; # 10
SELECT * FROM crm_configurations where id = 872; # 921 - In Professional Development , portalId: 9238273
SELECT * FROM opportunities WHERE team_id = 921 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 921 and updated_at > '2026-02-04 12:30:00' order by updated_at desc; # 2
# [PASSWORD_DOTS]
SELECT * FROM crm_configurations where id = 550; # 576 - SeedLegals , portalId: 3028661
SELECT * FROM opportunities WHERE team_id = 576 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 576 and updated_at > '2026-02-05 14:00:00' order by updated_at desc;
SELECT * FROM crm_configurations where id = 989; # 1058 - rtaoutdoor.com , portalId: 22371204
SELECT * FROM opportunities WHERE team_id = 1058 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 1058 and updated_at > '2026-02-05 14:00:00' order by updated_at desc;
SELECT * FROM crm_configurations where id = 896; # 946 - Mintago , portalId: 6621281
SELECT * FROM opportunities WHERE team_id = 946 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 946 and updated_at > '2026-02-05 14:00:00' order by updated_at desc;
SELECT * FROM crm_configurations where id = 617; # 641 - PCS , portalId: 5244937
SELECT * FROM opportunities WHERE team_id = 641 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 641 and updated_at > '2026-02-05 14:00:00' order by updated_at desc; # 7th
# [PASSWORD_DOTS]
SELECT * FROM crm_configurations where id = 649; # 670 - Eventeny , portalId: 4492849
SELECT * FROM opportunities WHERE team_id = 670 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 670 and updated_at > '2026-02-09 08:00:00' order by updated_at desc; #
SELECT * FROM crm_configurations where id = 48; # 51 - CleanCloud , portalId: 4373137
SELECT * FROM opportunities WHERE team_id = 51 and updated_at > '2026-03-04 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 51 and updated_at > '2026-02-09 08:00:00' order by updated_at desc;
select * from users where team_id = 51; # 7783
SELECT * FROM groups WHERE uuid_to_bin('8a8d2cb6-8b55-4fa3-8b5c-5f0e3d8de59a') = uuid; # 1130
select * from activity_searches where user_id = 7783;
select * from activity_search_filters where activity_search_id IN (32291, 32292);
SELECT asf.activity_search_id, asf.id, asf.value
FROM activity_search_filters asf
WHERE asf.filter = 'group_id'
AND asf.value IN (
SELECT CONCAT(
HEX(SUBSTR(uuid, 5, 4)), '-',
HEX(SUBSTR(uuid, 3, 2)), '-',
HEX(SUBSTR(uuid, 1, 2)), '-',
HEX(SUBSTR(uuid, 9, 2)), '-',
HEX(SUBSTR(uuid, 11))
)
FROM groups
WHERE deleted_at IS NOT NULL
);
SELECT * FROM crm_configurations where id = 272; # 290 - Bonham & Brook , portalId: 5705856
SELECT * FROM opportunities WHERE team_id = 290 and updated_at > '2026-02-05 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 290 and updated_at > '2026-02-09 08:00:00' order by updated_at desc; # 6th
# [PASSWORD_DOTS]
SELECT * FROM crm_configurations where provider = 'hubspot';
SELECT * FROM crm_configurations where id = 1056; # 1119 - Chromatic , portalId: 45602133
SELECT * FROM opportunities WHERE team_id = 1119 and remotely_created_at > '2026-02-01 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 1119 and updated_at > '2026-02-09 09:00:00' order by updated_at desc; # null
# [PASSWORD_DOTS]
select * from contacts where crm_provider_id = '003Uu00000ojD4NIAU';
select
cp.*
# DISTINCT t.id
# cp.id, cp.user_id, t.id, cp.crm_configuration_id, cp.contact_fields
FROM crm_profiles cp
JOIN crm_configurations crm on crm.id = cp.crm_configuration_id
JOIN users u on u.id = cp.user_id
JOIN teams t ON t.id = crm.team_id
WHERE crm.provider = 'salesforce' and t.status = 'active'
and cp.archived_at IS NULL and u.deleted_at IS NULL
and t.id NOT IN (1093)
and t.id = 2
and cp.contact_fields IS NULL;
# and c.crm_provider_id = '003Uu00000ojD4NIAU';
SELECT * FROM users WHERE id = 26484;
SELECT * FROM crm_profiles WHERE user_id = 26484;
SELECT * FROM social_accounts WHERE sociable_id = 26484;
SELECT * FROM crm_configurations where provider = 'salesforce';
select * from users where id IN (10022, 10403);
select * from users where team_id IN (526);
select * from teams where id IN (526, 532);
select * from crm_configurations where id IN (500, 516);
select * from crm_profiles where crm_configuration_id IN (500, 516) and user_id IN (10022, 10403);
select * from contacts where crm_configuration_id IN (500, 516) and crm_provider_id = '003Uu00000ojD4NIAU';
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 526 and sa.provider = 'salesforce';
select * from team_settings where team_id IN (526, 532);
select * from users where id IN (22824);
select * from crm_profiles where crm_configuration_id IN (1026);
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 1093 and sa.provider = 'salesforce';
select * from teams where id = 1099;
select * from users where id = 29643
select * from activity_processing_states;
SELECT * FROM teams where name LIKE '%Fare%'; # 233
SELECT * FROM opportunities where crm_configuration_id = 215
# and crm_provider_id = 'oppo_ogESZf2P50nDrd1nGPvKDXeA6sSaTN5v51Lp4ayVzKR'
;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 1088 and sa.provider = 'hubspot';
SELECT * FROM teams order by updated_at DESC
SELECT * FROM crm_configurations WHERE id = 1019; # SimpleConsign 1088 - no social account
select * from crm_configurations where provider = 'pipedrive';
select * from teams where id = 957;
select * from crm_configurations where id = 957;
SELECT * FROM teams WHERE name LIKE '%Prolific%'; # 544, 518, 10743
SELECT * FROM opportunities where crm_configuration_id = 518 order by id desc;
select * from users where team_id = 1; # 26726 - Gabriela Dureva
SELECT * FROM opportunities where user_id = 26726; # 16834447 - Prolific
select * from activities where user_id = 26726 order by id desc;
select * from contacts where crm_configuration_id = 1
and email IN ('[EMAIL]', '[EMAIL]'); # 2094416, 2093620
SELECT * FROM contacts WHERE id = 6284931;
SELECT p.* FROM activities a JOIN participants p ON a.id = p.activity_id
WHERE a.user_id = 26726 and p.lead_id IN (2094416, 2093620) and a.created_at > '2026-01-01 00:00:00' order by p.email;
select * from activities where id IN (75509259,75509261,75509261,75511034,75026464,75517602,75517605);
select * from crm_configurations where id = 1;
43801692-1aeb-32ce-acba-5b80a479701a
44c3c9cf-6f5e-75f3-8179-bc9f75dd2b1b
405975c0-b3d0-7aaa-821f-09d59cae6dd1
4caf848d-4bed-2299-b248-7788d41f9fca
49bedc3f-f196-eef3-89c3-dea6a3b4aa63
43420989-a09d-b8f8-9806-c8bbf7a02aac
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 1 and sa.provider = 'salesforce';
SELECT * FROM activities WHERE id = 75461988;
SELECT * FROM activities WHERE uuid_to_bin('d6c5052e-e972-49e9-8912-26f2f7d6c5f6') = uuid;
select * from contacts where id = 17900517;
select * from contact_roles cr join crm_configurations crm on cr.crm_configuration_id = crm.id
where crm.provider != 'salesforce';
select * from users where id = 21047;
SELECT * FROM crm_configurations WHERE id = 892;
SELECT * FROM teams WHERE id = 942;
select * from opportunities where team_id = 942 order by updated_at desc;
select * from contacts where team_id = 942 order by updated_at desc;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 942 and sa.provider = 'hubspot';
SELECT * FROM opportunities where team_id = 1 and crm_provider_id IN ('006Pq00000NeH6XIAV', '006Pq000007z8kdIAA'); # 10697889, 6621430
SELECT * FROM crm_configurations WHERE id = 1;
SELECT * FROM teams WHERE crm_id = 1;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 1 and sa.provider = 'salesforce';
select id, user_id, opportunity_fields from crm_profiles where crm_configuration_id = 1
SELECT * FROM opportunities where team_id = 1 order by updated_at desc; # 10697889, 6621430
select * from teams where id = 852;
select * from groups where id = 2286;
select * from sidekick_settings where team_id = 852;
select * from default_activity_types where team_id = 852;
SELECT cc.provider, cc.id, p.id, u.*
FROM users u
LEFT JOIN crm_profiles p ON u.id = p.user_id AND p.id IS NULL -- no profile
INNER JOIN teams t ON u.team_id = t.id AND t.status = 'active' -- team is active
INNER JOIN crm_configurations cc ON t.crm_id = cc.id
WHERE u.status = 1 AND u.deleted_at IS NULL
AND u.crm_required = 1
AND u.team_id = 1
ORDER BY u.team_id;
SELECT * FROM crm_profiles cp where cp.crm_configuration_id = 1 and cp.user_id IN (
18481
);
SELECT cc.provider, cc.id, p.id, u.*
FROM users u
LEFT JOIN crm_profiles p ON u.id = p.user_id
INNER JOIN teams t ON u.team_id = t.id AND t.status = 'active'
INNER JOIN crm_configurations cc ON t.crm_id = cc.id
WHERE u.status = 1
AND u.deleted_at IS NULL
AND u.crm_required = 1
# AND u.team_id = 1
AND p.id IS NULL -- Move this condition to WHERE clause
ORDER BY u.team_id;
SELECT * FROM opportunities WHERE id = 20002609;
select * from teams where id = 1122; # Velatir, 29953 - [EMAIL]
select * from crm_configurations where id = 1060;
select * from crm_layouts where crm_configuration_id = 1060;
SELECT * FROM crm_layout_entities WHERE crm_layout_id = 3596;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 1122 and sa.provider = 'hubspot';
select * from opportunities where team_id = 1122 order by updated_at desc;
select * from crm_field_data where object_type = 'contact';
SELECT * FROM activities WHERE uuid_to_bin('374fc8ed-3315-4c9f-9b25-318b7fd2928f') = uuid; # 76584262
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 248 and sa.provider = 'salesforce';
SELECT * FROM crm_profiles where user_id = 24115; # 005QF000002CswMYAS
SELECT * FROM users where id = 24115;
SELECT * FROM accounts where id = 4002896;
SELECT * FROM teams WHERE name LIKE '%adswerve%';
SELECT * FROM opportunities where crm_configuration_id = 230 AND crm_provider_id IN ("0069N000003GIQ9QAO","0061r000019yGP9AAM","0066900001S2KWlAAN","0066900001TDpj2AAD","0066900001b8uEwAAI","0069N000001rQi0QAE","006QF00000KD40mYAD","006QF00000LzpRJYAZ","0069N000002uomtQAA","0069N000002xlMLQAY","0066900001NV6ubAAD","0061r00001HJp45AAD","006QF00000uTlUoYAK","006QF00000v0bZqYAI");
SELECT * FROM opportunities WHERE crm_configuration_id = 230 AND crm_provider_id = '0069N000003GIQ9QAO'; # 6272203
SELECT u.id, u.email, ac.name, a.* FROM activities a
JOIN users u ON a.user_id = u.id
JOIN accounts ac ON a.account_id = ac.id
WHERE
uuid_to_bin('e3269598-b562-44fb-b5e9-9d2694dc63e0') = a.uuid or
uuid_to_bin('66ddc3ab-4e15-45aa-af0c-248c1eece593') = a.uuid or
uuid_to_bin('826bd328-e1cc-4213-b8d8-572454cacc07') = a.uuid;
select * from users where id = 5825;
SELECT * FROM activities WHERE uuid_to_bin('e56aa2e8-231a-421b-ab1f-cb38ed2bf573') = uuid;
select * from activities where uuid_to_bin('91e13b2f-2d1b-45f8-b1fd-1141b6563782') = uuid;
19594, 862
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 862 and sa.provider = 'salesforce';
select * from automated_reports where id = 36;
select ar.frequency, r.*, ar.* from automated_report_results r
join automated_reports ar on r.report_id = ar.id
where ar.frequency != 'one_off';
select s.* from activity_searches s join users u ON s.user_id = u.id where u.team_id = 882;
select * from nudges n where n.activity_search_id
select * from teams where created_at > '2026-03-09';
SELECT * FROM crm_layouts WHERE crm_configuration_id = 1065; # 1065
SELECT * FROM crm_layout_entities WHERE crm_layout_id = 3617;
select * from users where team_id = 1 and name like '%Lukas%'; # 7160
SELECT * FROM teams WHERE id = 575;
select * from opportunities where team_id = 575;
SELECT * FROM teams WHERE name LIKE '%Integrum ESG%'; # 1126, 1065,
select * from opportunities where team_id = 1126;
SELECT * FROM teams WHERE name LIKE '%Base%'; # 1125, 1063,
select * from opportunities where team_id = 1125;
select * from contacts c
where c.team_id = 882;
SELECT * FROM activities WHERE id = 76822967;
SELECT * FROM crm_profiles WHERE user_id = 15440;
SELECT * FROM crm_profiles WHERE crm_configuration_id = 555;
SELECT * FROM crm_configurations WHERE id = 555;
SELECT * FROM users WHERE id = 15440; # team. 581, gr. 15440, pl. 3911, act. field 162182
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 581 and sa.provider = 'salesforce';
SELECT * FROM automated_report_results order by id desc;
select * from features;
select * from team_features where feature_id = 40;
select * from teams where id = 556;
select * from automated_reports;
where id = 54; # 4fdd41f6-dcf0-30d0-b339-7345381b6044 , ["pdf","podcast"]
SELECT * FROM automated_report_results WHERE uuid_to_bin('822fa41b-afd3-43a9-a248-86b0e36f3131') = uuid;
select * from automated_report_results order by id desc;
SELECT * FROM automated_report_results WHERE id = 1919;
select * from automated_report_results WHERE report_id = 54;
select * from opportunities where id = 7594349;
SELECT * FROM teams WHERE name LIKE '%Les%'; # 711, 692, 16067 - [EMAIL]
select * from playbooks where team_id = 711; # event 226147
SELECT * FROM playbook_categories WHERE playbook_id = 5515;
SELECT * FROM crm_fields WHERE crm_configuration_id = 692 and object_type = 'event';
SELECT * FROM crm_fields WHERE id = 226147;
SELECT * FROM crm_field_values WHERE crm_field_id = 226147;
SELECT * FROM crm_configurations WHERE id = 692;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 711 and sa.provider = 'salesforce';
SELECT * FROM crm_profiles cp JOIN users u on u.id = cp.user_id WHERE u.team_id = 711;
select * from leads;
select * from calendars;
SELECT
t.id AS team_id,
t.name,
LOWER(SUBSTRING_INDEX(c.calendar_provider_id, '@', -1)) AS calendar_domain
FROM teams t
JOIN users u ON u.team_id = t.id
JOIN calendars c ON c.user_id = u.id AND c.status = 'active' AND c.calendar_provider_id LIKE '%@%'
LEFT JOIN team_domains td
ON td.team_id = t.id
AND td.deleted_at IS NULL
AND td.domain = LOWER(SUBSTRING_INDEX(c.calendar_provider_id, '@', -1))
GROUP BY t.id, t.name, calendar_domain
ORDER BY t.name, calendar_domain;
select * from users u join calendars c on c.user_id = u.id
where u.team_id = 882;
select * from activities where id = 74049485; # team 563 crm 537
select * from activities where id = 73272382; # team 563 crm 537
select * from activities where id = 64400389; # team 563 crm 537
select * from activities where id = 58081273; # team 563 crm 537
select * from activities where id = 54520297; # team 563 crm 537
select * from participants where activity_id = 58081273;
select * from activities where crm_configuration_id = 537 and provider = 'aircall'
and account_id = 19003658 order by updated_at desc;
select * from contacts where crm_configuration_id = 537 and id = 35957759;
select * from accounts where crm_configuration_id = 537 and id = 19003658;
select * from automated_report_results where id = 1976;
select * from automated_reports where id = 583;
select * from activity_searches where id = 87714;
select * from activity_search_filters where activity_search_id = 87714;
SELECT * FROM activities WHERE uuid_to_bin('8827f672-202d-4162-9d04-73ff5f0566a9') = uuid
or uuid_to_bin('47842446-af51-4bcb-854f-cc6560290101') = uuid;
SELECT * FROM crm_configurations WHERE provider = 'hubspot';
select * from rate_limits;
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<?php
declare(strict_types=1);
namespace Jiminny\Models;
use Illuminate\Database\Eloquent\Relations\MorphTo;
use Jiminny\Component\Eloquent\Builder;
use Jiminny\Contracts\Http\RateLimitInterface;
/**
* Jiminny\Models\RateLimit
*
* @property int $id
* @property \Illuminate\Support\Carbon|null $created_at
* @property \Illuminate\Support\Carbon|null $updated_at
* @property int $quota
* @property int $window
* @property int $limited_id
* @property string $limited_type
* @property-read \Illuminate\Database\Eloquent\Model $limited
*
* @method static Builder|RateLimit chunkByIdDesc($count, callable $callback, $column = null, $alias = null)
* @method static Builder|RateLimit newModelQuery()
* @method static Builder|RateLimit newQuery()
* @method static Builder|RateLimit query()
* @method static Builder|RateLimit whereCreatedAt($value)
* @method static Builder|RateLimit whereId($value)
* @method static Builder|RateLimit whereLimitedId($value)
* @method static Builder|RateLimit whereLimitedType($value)
* @method static Builder|RateLimit whereQuota($value)
* @method static Builder|RateLimit whereUpdatedAt($value)
* @method static Builder|RateLimit whereWindow($value)
*
* @mixin \Eloquent
*/
class RateLimit extends Model implements RateLimitInterface
{
protected $fillable = [
'quota',
'window',
];
public function limited(): MorphTo
{
return $this->morphTo();
}
public function getQuota(): int
{
return $this->attributes['quota'];
}
public function getWindow(): int
{
return $this->attributes['window'];
}
public function getKey(): string
{
$key = sprintf(
'%s.%s.%s.%s',
$this->limited_id,
$this->limited_type,
$this->getQuota(),
$this->getWindow()
);
return base64_encode($key);
}
}
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HubSpot rate limit implementation strategy
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Claude is responding
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
Show more
10:42
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Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
Copy
Give positive feedback
Give negative feedback
You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
Edit
Copy
Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
Copy to clipboard
php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.
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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
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Claude is AI and can make mistakes. Please double-check responses.
Claude is AI and can make mistakes. Please double-check responses....
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Claude is responding
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
Show more
10:42
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Copy
Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
Copy
Give positive feedback
Give negative feedback
You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
Edit
Copy
Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically...
|
Claude
|
Claude
|
NULL
|
1942
|
|
1941
|
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Share chat
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)....
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Claude
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Claude
|
NULL
|
1941
|
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1940
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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
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Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
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You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
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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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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?
Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?
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Claude 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
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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
(...
|
Claude
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Claude
|
NULL
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1939
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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
Show more
10:42
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Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
Copy
Give positive feedback
Give negative feedback
Retry
You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
Retry
Edit
Copy
Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
Copy to clipboard
php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds...
|
Claude
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Claude
|
NULL
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1938
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1937
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Wispr FlowFileDictationViewEditHelpHubSpot rate l Wispr FlowFileDictationViewEditHelpHubSpot rate limit implementation strategyWindowuntouchedl• Burst → Search: No. Saturating burst leaves Search's 5/s untouched• Either → Daily: Yes, but only by contributing to the sankills you here, not the type.In theory you can sustain ~50 search + ~190 other = ~240 d(until daily runs out), because the two buckets bill indepenImplementation consequenceYour rate limiter needs to know which bucket a request hitDauleril.Slimiter-›acouire"search', Swerghts):$limiter->acquire('burst', $weight=1);$limiter->acquire('daily', $weight=1);// for se// for e// everyn Redis: three senarate kevs, three senarate Lua acquire siparameter). A search call calls acquire('search') + acqcalis acauire('burst' + acauiredanv').One subtle pitfall: when you backoff on a 429, inspect polknow which bucket actually fired. If you see SECONDLY fro:to delay search-bound jobs — burst-bound jobs in the queunause evervthing on anv 429. vou waste capacity on the bi(or two throttle keys) make this clean; one global pause isThe other subtlety: the burst response gives you X-HubSpoocal sare aoainst. our searen coesn'"rerurn tnose neader.unverified — be slightly more conservative there (e.g., budunaccountee-tor callsKeep going in Claude CodeSwitch to Claude Code and let Claude work directly in yrunning and testing as it goes.Ibl Flow (Basioo Homelol InsightsD Dictionary* SnippetsTr Style*8. Transforms• Scratchpad1701 words remainingVoukUpoodwortnimifedUpgrade to Pro%, Invite your team# Get a free monthộ Settings® HelpOk, so what will happen each time I make a request? Let's say I have 100,000 requests to make thatare inctant Dolneed to inerement each requect haced on the tune? For eyamnle ifthere ic asearch request, I need to increment both the search bucket and also the daily bucket. If it's aburst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I needto see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure,adding additional load to each request?Opus 4.7 AdaptiveClaude is Al and can make mistakes. Please double-check responses.InsightsYour Usage Your VoiceA HS_Jocal (jiminny@localhost]A console [PROD] X A console [EU]CascadeHubspot Rate Limiting$0Support Daily - in 1h 16m100% 12Thu 7 May 13:44:09AsKJiminnykepoпtAcuivityservice lest+0 ..ntil is deadline, not delav. Job continues retrvina until now > retrvUnti10.rrom RateLimit Excent ionInsights building...Will notify you when it's ready!bin('8827f672-202d-4162-9d04-73ff5f0566a9') = uuid-C66560290101') = uuid;provider = 'hubspot':rns the seconds to wait right now before this specitic attemot should retry. Comes from:Limiter:: requestavallablein → max or all bucket avallableino valuesn HubSpot's 429 response Retry-After headerUpdate ready! ®Absolute deadline Datelime)All failures (rate limit + real errors)Keep retrying with backoff() delays until deadlineYes (increments attempt count)gether in DeleteCrmEntityTrait:te limit → HandleRateLimit middleware catches itback to queue, attempt count stays the sameTter 10 seconds3 times with RFAI errors (not rate limit).ries, checks retryUntil()1 hour, keeps retryinghour. iob marked failedgetRetryAtterRelative delay int secondsOnly RateLimitExceptionRelease immediately with specific delayNo (release() doesn't increment attempts)-1 hour): Ensures we don't give up on deletes due to temporary issues (including sustained rate limiting)mit + aetRetrvAfter@: Ensures we don't burn through stries durina rate limit storms11, 3 rate limit hits would exhaust stries = 3 and fail the job. With retryUntil, those rate limit releases don't count against the try• can keen trving for an hour1| 14 files with changesapp/Jobs/Middleware/ HandleRateLimit.php +42ann/lohelCrm/Doloto/ DolotoCrmEntitvTrait nhn t18Reiect alllOK review documatation again to make sure we cover all rate limits there are for hubspot. Lets add support for different rate limit and< CodeTall aView allAccent allif 4 spaces...
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iTerm2ShellEditViewSessionScriptsProfilesWindowHel iTerm2ShellEditViewSessionScriptsProfilesWindowHelpDOCKERLast login: Thu May₴1DEV (-zsh)7 09:45:09on ttys010₴82APP (-zsh)Poetry could not find a pyproject.toml file in /Users/lukas or its parentsPoetry could not find a pyproject.tomlfile in /Users/lukas or its parentslukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~$cd ~/.screenpipelukas@Lukas-Kovaliks-MacBook-Pro-Jiminny~/.screenpipe $ 11total667416drwxr-xr-xdrwx-drwxr-xr-x11lukasstaff3527 May13:4093lukasstaff29767 May13:4018lukasstaff5766May20:31datalukasstaff3361546247May13:40db.sqlite-rw-r--r--lukasstaff655367May10:42db.sqlite-shm-rw-r--r--lukasstaff44084327May13:40db.sqlite-waldrwxr-xr-x8lukasstaff2566May20:27pipes-rw-r--r--lukasstaff284086May21:02screenpipe.2026-05-06.0.10g-rw-r--r--lukasstaff1594697May13:40screenpipe.2026-05-07.0.10g-rwxr-xr-xlukasstaff149946May20:26-rw-r--r--1 lukasstaff3167screenpipe_sync.sh7 May 09:23 sync. loglukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/.screenpipe $ du -sh ~/.screenpipe449M/Users/lukas/.screenpipelukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/.screenpipe $ П-zshallSupport Daily - in 1h 16 mA100% <478Thu 7 May 13:44:08181-zsh• 84|screenpipe*•$5-zsh...
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Wispr FlowFileDictationViewEditHelpHubSpot rate l Wispr FlowFileDictationViewEditHelpHubSpot rate limit implementation strategyWindowuntouchedl• Burst → Search: No. Saturating burst leaves Search's 5/s untoucheo• Either → Daily: Yes, but only by contributing to the sankills you here, not the type.In theory you can sustain ~50 search + ~190 other = ~240 g(until daily runs out), because the two buckets bill indepenImplementation consequenceYour rate limiter needs to know which bucket a request hitDauleril.Slimiter-›acouire"search', Sweight=:$limiter->acquire('burst', $weight=1);$limiter->acquire('daily', $weight=1);// for se// for e// everyn Redis: three senarate kevs, three senarate Lua acquire siparameter). A search call calls acquire('search') + acqcais acauire('burst") + acauiredaiv').One subtle pitfall: when you backoff on a 429, inspect polknow which bucket actually fired. If you see SECONDLY fro:to delay search-bound jobs — burst-bound jobs in the queunause evervthing on anv 429. vou waste capacity on the bi(or two throttle keys) make this clean; one global pause isThe other subtlety: the burst response gives you X-HubSpoocal sare aoainst. our searen coesn'"rerurn tnose neader.unverified — be slightly more conservative there (e.g., budunaccountee-tor callsKeep going in Claude CodeSwitch to Claude Code and let Claude work directly in yrunning and testing as it goes.Ibl Flow (Basioo Homelol InsightsD Dictionary* SnippetsTr Style*8. Transforms• Scratchpad1701 words remainingweek. Upgrade for unlimitedUpgrade to Pro%, Invite your team# Get a free monthộ Settings® HelpOk, so what will happen each time I make a request? Let's say I have 100,000 requests to make thatare inctant Dolneed to inerement each requect haced on the tune? For eyamnle ifthere ic asearch request, I need to increment both the search bucket and also the daily bucket. If it's aburst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I needto see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure,adding additional load to each request?Opus 4.7 AdaptiveClaude is Al and can make mistakes. Please double-check responses.A HS_Jocal (jiminny@localhost] console [PROD] x 4 console (EU)CascadeHubspot Rate Limiting$0Support Daily - in 1h 16m100% 12Thu 7 May 13:44:08AsKJiminnykepoпtAcuivityservice lestntil is deadline, not delav. Job continues retrvina until now > retrvUnti10.(from RateLimitException):+0 ..InsightsYour Usage Your VoiceYour Voice Profile is ready!Generate profileYour dictations are private and only stored locally. Never shared or stored by Wispr Flow. Your report is based on local data.bin('8827f672-202d-4162-9d04-73ff5f0566a9') = uuid-C66560290101') = uuid;provider = 'hubspot';rns the seconds to wait right now before this specitic attemot should retry. Comes from:Limiter:: requestavallablein → max or all bucket avallableino valuesn HubSpot's 429 response Retry-After headerUpdate ready! OAbsolute deadline Datelime)All failures (rate limit + real errors)Keep retrying with backoff() delays until deadlineYes (increments attempt count)gether in DeleteCrmEntityTrait:te limit → HandleRateLimit middleware catches itback to queue, attempt count stays the sameTter 10 seconds3 times with RFAI errors (not rate limit).ries, checks retryUntil()1 hour, keeps retryingihour. iob marked failedgetRetryAtterRelative delay int secondsOnly RateLimitExceptionRelease immediately with specific delayNo (release() doesn't increment attempts)-1 hour): Ensures we don't give up on deletes due to temporary issues (including sustained rate limiting)mit + aetRetrvAfter@: Ensures we don't burn through stries durina rate limit storms11, 3 rate limit hits would exhaust stries = 3 and fail the job. With retryUntil, those rate limit releases don't count against the try• can keen trving for an hour1| 14 files with changesapp/Jobs/Middleware/ HandleRateLimit.php +42app/Jobs/Crm/Delete/[ DeleteCrmEntityTrait.php +18Reiect alllOK review documatation again to make sure we cover all rate limits there are for hubspot. Lets add support for different rate limit and<> Code64-22UTF-8Tall aView allAccent allif 4 spaces...
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iTerm2ShellEditViewSessionScriptsProfilesWindowHel iTerm2ShellEditViewSessionScriptsProfilesWindowHelpDOCKERLast login: Thu May₴1DEV (-zsh)7 09:45:09on ttys010₴82APP (-zsh)Poetry could not find a pyproject.toml file in /Users/lukas or its parentsPoetry could not find a pyproject.tomlfile in /Users/lukas or its parentslukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~$cd ~/.screenpipelukas@Lukas-Kovaliks-MacBook-Pro-Jiminny~/.screenpipe $ 11total667416drwxr-xr-xdrwx-drwxr-xr-x11lukasstaff3527 May13:4093lukasstaff29767 May13:4018lukasstaff5766May20:31datalukasstaff3361546247May13:40db.sqlite-rw-r--r--lukasstaff655367May10:42db.sqlite-shm-rw-r--r--lukasstaff44084327May13:40db.sqlite-waldrwxr-xr-x8lukasstaff2566May20:27pipes-rw-r--r--lukasstaff284086May21:02screenpipe.2026-05-06.0.10g-rw-r--r--lukasstaff1594697May13:40screenpipe.2026-05-07.0.10g-rwxr-xr-xlukasstaff149946May20:26-rw-r--r--1 lukasstaff3167screenpipe_sync.sh7 May 09:23 sync. loglukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/.screenpipe $ du -sh ~/.screenpipe449M/Users/lukas/.screenpipelukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/.screenpipe $ П-zshallSupport Daily - in 1h 16 mA100% <478Thu 7 May 13:44:05181-zsh• 84|screenpipe*•$5-zsh...
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Wispr FlowFileDictationViewEditHelpHubSpot rate l Wispr FlowFileDictationViewEditHelpHubSpot rate limit implementation strategyWindowuntouchedl• Burst → Search: No. Saturating burst leaves Search's 5/s untoucheo• Either → Daily: Yes, but only by contributing to the sankills you here, not the type.In theory you can sustain ~50 search + ~190 other = ~240 g(until daily runs out), because the two buckets bill indepenImplementation consequenceYour rate limiter needs to know which bucket a request hitDauleril.Slimiter-›acouire"search', Sweight=:§limiter->acquire('burst', $weight=1);$limiter->acquire('daily', $weight=1);// for se// for e// everyn Redis: three senarate kevs, three senarate Lua acquire siparameter). A search call calls acquire('search') + acqcais acauire('burst") + acauiredaiv').One subtle pitfall: when you backoff on a 429, inspect polknow which bucket actually fired. If you see SECONDLY fro:to delay search-bound jobs — burst-bound jobs in the queunause evervthing on anv 429. vou waste capacity on the bi(or two throttle keys) make this clean; one global pause isThe other subtlety: the burst response gives you X-HubSpoocal sare aoainst. our searen coesn'"rerurn tnose neader.unverified — be slightly more conservative there (e.g., budunaccountee-tor callsKeep going in Claude CodeSwitch to Claude Code and let Claude work directly in yrunning and testing as it goes.Ibl Flow (Basioo Homelol InsightsD Dictionary* SnippetsTr Style*8. Transforms# Scratchpad1701 words remainingYou get 2000 words perweek. Upgrade for unlimitedUpgrade to Pro%, Invite your team# Get a free monthSettings® HelpOk, so what will happen each time I make a request? Let's say I have 100,000 requests to make thatare inctant Dolneed to inerement each requect haced on the tune? For eyamnle ifthere ic asearch request, I need to increment both the search bucket and also the daily bucket. If it's aburst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I needto see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure,adding additional load to each request?Opus 4.7 AdaptiveClaude is Al and can make mistakes. Please double-check responses.A HS_Jocal (jiminny@localhost] console [PROD] x 4 console (EU)CascadeHubspot Rate Limiting$0Support Daily - in 1h 16m100% 12Thu 7 May 13:44:03AsKJiminnykepoпtAcuivityservice lestntil is deadline, not delav. Job continues retrvina until now > retrvUnti10.(from RateLimitException):+0 ..InsightsYour Usage Your VoiceYour Voice Profile is ready!Generate profileYour dictations are private and only stored locally. Never shared or stored by Wispr Flow. Your report is based on local data.bin('8827f672-202d-4162-9d04-73ff5f0566a9') = uuid-C66560290101') = uuid;provider = 'hubspot';rns the seconds to wait right now before this specitic attemot should retry. Comes from:Limiter:: requestavallablein → max or all bucket avallableino valuesn HubSpot's 429 response Retry-After headerUpdate ready! OAbsolute deadline Datelime)All failures (rate limit + real errors)Keep retrying with backoff() delays until deadlineYes (increments attempt count)gether in DeleteCrmEntityTrait:te limit → HandleRateLimit middleware catches itback to queue, attempt count stays the sameTter 10 seconds3 times with RFAI errors (not rate limit).ries, checks retryUntil()1 hour, keeps retryingihour. iob marked failedgetRetryAtteroRelative delay int secondsOnly RateLimitExceptionRelease immediately with specific delayNo (release() doesn't increment attempts)-1 hour): Ensures we don't give up on deletes due to temporary issues (including sustained rate limiting)mit + aetRetrvAfter@: Ensures we don't burn through stries durina rate limit storms11, 3 rate limit hits would exhaust stries = 3 and fail the job. With retryUntil, those rate limit releases don't count against the try• can keen trving for an hour1| 14 files with changesapp/Jobs/Middleware/ HandleRateLimit.php +42app/Jobs/Crm/Delete/[ DeleteCrmEntityTrait.php +18Reiect alllOK review documatation again to make sure we cover all rate limits there are for hubspot. Lets add support for different rate limit and<> Code64-22UTF-8Tall aView allAccent allif 4 spaces...
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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
Show more
10:42
Retry
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Copy
Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms....
|
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Claude
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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
Show more
10:42
Retry
Edit
Copy
Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After...
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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
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Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
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You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
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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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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?
Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?
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Claude is AI and can make mistakes. Please double-check responses.
Claude is AI and can make mistakes. Please double-check responses....
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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?
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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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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?
Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?
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Claude 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
Retry
Edit
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Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
Copy to clipboard
php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.
Copy
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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
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Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
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You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
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Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
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┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
Copy to clipboard
php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.
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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?
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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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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
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Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
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You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
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Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
Copy to clipboard
php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.
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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:
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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')
+
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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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
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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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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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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Claude
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Claude
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1922
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Last login: Thu May 7 09:45:09 on ttys010
Poetry Last login: Thu May 7 09:45:09 on ttys010
Poetry could not find a pyproject.toml file in /Users/lukas or its parents
Poetry could not find a pyproject.toml file in /Users/lukas or its parents
lukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~ $ cd ~/.screenpipe
lukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/.screenpipe $ ll
total 667416
drwxr-xr-x 11 lukas staff 352 7 May 13:40 .
drwx------+ 93 lukas staff 2976 7 May 13:40 ..
drwxr-xr-x 18 lukas staff 576 6 May 20:31 data
-rw-r--r-- 1 lukas staff 336154624 7 May 13:40 db.sqlite
-rw-r--r-- 1 lukas staff 65536 7 May 10:42 db.sqlite-shm
-rw-r--r-- 1 lukas staff 4408432 7 May 13:40 db.sqlite-wal
drwxr-xr-x 8 lukas staff 256 6 May 20:27 pipes
-rw-r--r-- 1 lukas staff 28408 6 May 21:02 screenpipe.2026-05-06.0.log
-rw-r--r-- 1 lukas staff 159469 7 May 13:40 screenpipe.2026-05-07.0.log
-rwxr-xr-x 1 lukas staff 14994 6 May 20:26 screenpipe_sync.sh
-rw-r--r-- 1 lukas staff 3167 7 May 09:23 sync.log
lukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/.screenpipe $ du -sh ~/.screenpipe
449M /Users/lukas/.screenpipe
lukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/.screenpipe $
DOCKER
Close Tab
DEV (-zsh)
Close Tab
APP (-zsh)
Close Tab
-zsh
Close Tab
screenpipe"
Close Tab
-zsh
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⌥⌘1
-zsh...
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iTerm2
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-zsh
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1921
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